ANOVA comparison of all datasets

We are assessing the comparability of cows across all provided data-sets for inclusion in a compiled data-set. A data-set of all cows in all datasets will be used to draw random samplings of cows that represent pens on a dairy. These simulated pens will be used to fit the distribution of Dry Matter Intake (DMI) and other group level parameters will train a model that predict this distribution function. Before we can combine these data-sets together it is important to validate that the cows across the different data-sets are comparable metabolically, so that the assessment of DMI and the plotting of DMI distribution across the constructed pen groups is appropriate.

ANOVA compares two or more groups for a difference in group means. This is performed by comparing the variance between groups against the variance within each group. If the variance between groups is significantly larger than the variance within each group then the groups are considered significantly different.

The means of the values for milk production (kg), DMI (kg) and feed efficiency (fat corrected milk(FCM) (kg)/DMI (kg)) by data-set will be compared using ANOVA statistical analysis. Pairwise comparison of the group means, and visual assessment of the marginal values of each of these three outcomes will be used to assess the comparability of the data-sets and guide decision criteria for inclusion in a collated data-set.

Data description

Number of cows in all data-sets

Table 1.

Milk production

This plot demonstrates how milk production (kg) changes by week across the lactation for all cows in each data-set. Figure 1.

Dry matter intake

This plot demonstrates how DMI (kg) changes by week across the lactation for all cows in each data-set. Figure 2.

Feed Efficiency

This plot demonstrates how Feed Efficiency (kg fat corrected milk/kg DMI)) changes by week across the lactation for all cows in each data-set. Figure 3.

Box-plots of milk yield (kg) by week for each data-set

Figure-set 4.

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Box-plots of DMI (kg) by week for each data-set

Figure-set 5.

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Box-plots of feed efficiency by week for each data-set

Figure-set 6.

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Hypothesis

\(H_0:\) the data-sets are equal in 1. milk production (kg), 2. DMI (kg), 3. feed efficiency
\(H_1:\) at least one mean is different between all data-sets

ANOVA methods

Assumptions

Variable type

Milk production (kg), DMI (kg) and feed efficiency (ratio) are all considered continuous variables. Groups to compare are data-set of cow origin which is a qualitative variable.

Independence

Between group observations are independent as data was collected from separate trials and sites by data-set. Within group observations are assumed independent as observations are on the level of cow with one measurement per time-point per cow and assumed no behavior interaction between cows as feed data was collected via individual feed bunks (Calan gates).

Normality

Residuals will be assessed for normal distribution. Each group sample size is \(n > 30\).

Equality of variance

Group variance differs (a significant levene test indicates unequal variance), but since data-set sample sizes are similar (Table 1) it is assumed that the ANOVA test is robust enough to accept this violation of the assumption. (Or use the Welch ANOVA which does not require this assumption).

Outliers

Outliers were assessed by box-plot descriptive analysis (Figure-set 4, 5, 6). No outliers were detected in milk production (kg) or DMI (kg).
In the feed efficiency variable an outlier of 9.18 was observed at week 35 for data-set 8.

This cow was traced back to the original database of that trial and a value of 10.9 was input as the milk fat yield (kg) at week 35. This is a biological impossibility and a data entry error so the feed efficiency value for this record at week 35 was removed from the ANOVA analysis.

Updated box-plot of week 35 feed efficiency
Min. 1st Qu. Median Mean 3rd Qu. Max. NA’s
0 1.076 1.267 1.225 1.406 2.412 266

Model equation

\[Y_{i,j} = μ + \beta_i+ϵ_{i,j}\]

\(Y_{i,j}\) is the \(j\)-th observation \((j = 1, 2, ..., n_i\)) of the \(i\)-th group (\(i = 1, 2, ..., 9\) levels).
\(μ\) is the common effect for the whole experiment.
\(\beta_i\) is the \(i\)-th treatment effect.
\(ϵ_{i,j}\) is the random error present in the \(j\)-th observation on the \(i\)-th treatment.

\(\sum \beta_i = 0, i = 1, ..., 9\) are considered fixed parameters and this is a fixed effects model.

Model fitting for milk (kg) as output variable

Week 1

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 6454.75 6 1075.79 27.65 < .001
Residuals 13148.65 338 38.90
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(6, 338) = 27.65, p < .001; Eta2 = 0.33, 95% CI [0.26, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 -2.4730757 -5.7667414 0.820590 0.2837237
3-1 5.7892787 1.8556481 9.722909 0.0003369
5-1 -0.1632628 -4.0076961 3.681170 0.9999997
6-1 -1.9877696 -6.1427492 2.167210 0.7913137
7-1 -9.0693725 -12.6657757 -5.472969 0.0000000
9-1 3.5124395 0.0156290 7.009250 0.0481612
3-2 8.2623544 4.5373514 11.987357 0.0000000
5-2 2.3098130 -1.3208709 5.940497 0.4903336
6-2 0.4853061 -3.4727305 4.443343 0.9998156
7-2 -6.5962968 -9.9632411 -3.229353 0.0000003
9-2 5.9855153 2.7251652 9.245865 0.0000021
5-3 -5.9525415 -10.1723853 -1.732698 0.0007184
6-3 -7.7770483 -12.2816315 -3.272465 0.0000106
7-3 -14.8586513 -18.8538396 -10.863463 0.0000000
9-3 -2.2768392 -6.1826167 1.628938 0.5969480
6-5 -1.8245068 -6.2514118 2.602398 0.8851151
7-5 -8.9061098 -12.8135063 -4.998713 0.0000000
9-5 3.6757023 -0.1402269 7.491632 0.0674953
7-6 -7.0816030 -11.2949076 -2.868298 0.0000204
9-6 5.5002091 1.3715890 9.628829 0.0018143
9-7 12.5818121 9.0158951 16.147729 0.0000000

NULL

Week 2

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 9955.76 6 1659.29 39.13 < .001
Residuals 14331.10 338 42.40
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(6, 338) = 39.13, p < .001; Eta2 = 0.41, 95% CI [0.34, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 1.4234040 -2.015173 4.8619806 0.8829569
3-1 5.2486805 1.141982 9.3553785 0.0033377
5-1 7.3196574 3.306081 11.3332337 0.0000025
6-1 2.1950976 -2.142688 6.5328833 0.7441524
7-1 -9.0167665 -12.771400 -5.2621329 0.0000000
9-1 7.9261956 4.275536 11.5768549 0.0000000
3-2 3.8252765 -0.063615 7.7141680 0.0572462
5-2 5.8962534 2.105831 9.6866760 0.0001137
6-2 0.7716936 -3.360484 4.9038715 0.9979425
7-2 -10.4401705 -13.955250 -6.9250912 0.0000000
9-2 6.5027917 3.098997 9.9065868 0.0000007
5-3 2.0709769 -2.334527 6.4764805 0.8046694
6-3 -3.0535829 -7.756354 1.6491878 0.4645121
7-3 -14.2654470 -18.436411 -10.0944830 0.0000000
9-3 2.6775151 -1.400104 6.7551346 0.4502685
6-5 -5.1245598 -9.746235 -0.5028849 0.0189467
7-5 -16.3364239 -20.415734 -12.2571143 0.0000000
9-5 0.6065382 -3.377280 4.5903564 0.9993546
7-6 -11.2118641 -15.610541 -6.8131873 0.0000000
9-6 5.7310980 1.420832 10.0413645 0.0018688
9-7 16.9429621 13.220156 20.6657683 0.0000000

NULL

Week 3

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 11937.27 8 1492.16 32.24 < .001
Residuals 17770.30 384 46.28
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(8, 384) = 32.24, p < .001; Eta2 = 0.40, 95% CI [0.34, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 2.5117112 -1.2664130 6.289835 0.4928067
3-1 4.9429649 0.4307448 9.455185 0.0198883
4-1 7.0005781 2.7348716 11.266285 0.0000171
5-1 10.2860927 5.8761897 14.695996 0.0000000
6-1 4.0151806 -0.7509464 8.781308 0.1784502
7-1 -7.5312819 -11.6566725 -3.405891 0.0000009
8-1 -1.0946566 -13.6766831 11.487370 0.9999991
9-1 10.0497551 6.0386060 14.060904 0.0000000
3-2 2.4312538 -1.8416522 6.704160 0.6987146
4-2 4.4888670 0.4771532 8.500581 0.0155998
5-2 7.7743815 3.6096679 11.939095 0.0000004
6-2 1.5034694 -3.0367466 6.043685 0.9824696
7-2 -10.0429931 -13.9051741 -6.180812 0.0000000
8-2 -3.6063678 -16.1045671 8.891832 0.9928851
9-2 7.5380440 3.7981359 11.277952 0.0000000
4-3 2.0576132 -2.6519256 6.767152 0.9109067
5-3 5.3431278 0.5025960 10.183659 0.0182519
6-3 -0.9277844 -6.0949373 4.239369 0.9997622
7-3 -12.4742468 -17.0570790 -7.891415 0.0000000
8-3 -6.0376216 -18.7769659 6.701723 0.8650396
9-3 5.1067902 0.6265200 9.587060 0.0125022
5-4 3.2855146 -1.3260870 7.897116 0.3931188
6-4 -2.9853976 -7.9387390 1.967944 0.6278776
7-4 -14.5318600 -18.8721909 -10.191529 0.0000000
8-4 -8.0952348 -20.7493651 4.558896 0.5473280
9-4 3.0491770 -1.1827188 7.281073 0.3770414
6-5 -6.2709121 -11.3489613 -1.192863 0.0043054
7-5 -17.8173746 -22.2995019 -13.335247 0.0000000
8-5 -11.3807493 -24.0842137 1.322715 0.1202560
9-5 -0.2363376 -4.6135438 4.140869 1.0000000
7-6 -11.5464625 -16.3794932 -6.713432 0.0000000
8-6 -5.1098372 -17.9413114 7.721637 0.9465239
9-6 6.0345746 1.2986843 10.770465 0.0026958
8-7 6.4366253 -6.1708968 19.044147 0.8085068
9-7 17.5810370 13.4906168 21.671457 0.0000000
9-8 11.1444118 -1.4261921 23.715016 0.1292001

NULL

Week 4

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 13213.25 8 1651.66 36.09 < .001
Residuals 18395.42 402 45.76
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(8, 402) = 36.09, p < .001; Eta2 = 0.42, 95% CI [0.35, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 2.2424098 -1.5135821 5.9984017 0.6403778
3-1 5.3759946 0.8902071 9.8617822 0.0065401
4-1 7.8468478 3.6061298 12.0875658 0.0000006
5-1 10.0730838 5.6890140 14.4571535 0.0000000
6-1 4.3054981 -0.4327088 9.0437051 0.1088288
7-1 -7.8443076 -11.9455316 -3.7430836 0.0000002
8-1 -1.0733611 -6.4851743 4.3384521 0.9995031
9-1 10.3718147 6.3841629 14.3594665 0.0000000
3-2 3.1335848 -1.1142905 7.3814602 0.3441393
4-2 5.6044380 1.6162249 9.5926511 0.0005052
5-2 7.8306740 3.6903573 11.9709907 0.0000003
6-2 2.0630883 -2.4505311 6.5767077 0.8875458
7-2 -10.0867174 -13.9262738 -6.2471610 0.0000000
8-2 -3.3157709 -8.5320796 1.9005378 0.5565525
9-2 8.1294049 4.4114052 11.8474046 0.0000000
4-3 2.4708531 -2.2110972 7.1528035 0.7786910
5-3 4.6970891 -0.1150868 9.5092651 0.0619267
6-3 -1.0704965 -6.2073803 4.0663872 0.9992852
7-3 -13.2203023 -17.7762882 -8.6643163 0.0000000
8-3 -6.4493557 -12.2134421 -0.6852694 0.0155933
9-3 4.9958200 0.5417953 9.4498448 0.0151485
5-4 2.2262360 -2.3583508 6.8108228 0.8484271
6-4 -3.5413497 -8.4656744 1.3829751 0.3800009
7-4 -15.6911554 -20.0060607 -11.3762501 0.0000000
8-4 -8.9202089 -14.4956991 -3.3447187 0.0000316
9-4 2.5249669 -1.6821385 6.7320723 0.6336442
6-5 -5.7675857 -10.8158876 -0.7192837 0.0121381
7-5 -17.9173914 -22.3732624 -13.4615204 0.0000000
8-5 -11.1464449 -16.8317302 -5.4611595 0.0000001
9-5 0.2987309 -4.0528337 4.6502955 0.9999999
7-6 -12.1498057 -16.9545246 -7.3450868 0.0000000
8-6 -5.3788592 -11.3414935 0.5837750 0.1146230
9-6 6.0663166 1.3581691 10.7744640 0.0022588
8-7 6.7709465 1.3008057 12.2410873 0.0041512
9-7 18.2161223 14.1496637 22.2825809 0.0000000
9-8 11.4451758 6.0596610 16.8306905 0.0000000

NULL

Week 5

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 14020.28 8 1752.53 38.60 < .001
Residuals 18432.48 406 45.40
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(8, 406) = 38.60, p < .001; Eta2 = 0.43, 95% CI [0.37, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 3.1027092 -0.6382959 6.8437142 0.1954952
3-1 6.5315699 2.0636812 10.9994587 0.0002331
4-1 7.9987433 3.7749463 12.2225404 0.0000003
5-1 10.2344234 5.8678466 14.6010003 0.0000000
6-1 3.9636632 -0.7556378 8.6829642 0.1818097
7-1 -7.7011602 -11.7860199 -3.6163005 0.0000003
8-1 -2.1248708 -7.1934272 2.9436857 0.9288607
9-1 10.8635888 6.8918482 14.8353295 0.0000000
3-2 3.4288607 -0.8020651 7.6597866 0.2219578
4-2 4.8960341 0.9237344 8.8683338 0.0044129
5-2 7.1317142 3.0079178 11.2555106 0.0000042
6-2 0.8609540 -3.6346556 5.3565636 0.9996168
7-2 -10.8038694 -14.6281055 -6.9796332 0.0000000
8-2 -5.2275800 -10.0885437 -0.3666163 0.0243328
9-2 7.7608796 4.0577151 11.4640441 0.0000000
4-3 1.4671734 -3.1960954 6.1304422 0.9874235
5-3 3.7028535 -1.0901214 8.4958283 0.2816361
6-3 -2.5679068 -7.6842938 2.5484803 0.8230979
7-3 -14.2327301 -18.7705372 -9.6949230 0.0000000
8-3 -8.6564407 -14.0966502 -3.2162312 0.0000360
9-3 4.3320189 -0.1042339 8.7682716 0.0617002
5-4 2.2356801 -2.3306137 6.8019740 0.8424254
6-4 -4.0350801 -8.9397563 0.8695960 0.2045592
7-4 -15.6999035 -19.9975918 -11.4022152 0.0000000
8-4 -10.1236141 -15.3652084 -4.8820198 0.0000001
9-4 2.8648455 -1.3254731 7.0551641 0.4528231
6-5 -6.2707603 -11.2989190 -1.2426016 0.0037164
7-5 -17.9355836 -22.3736752 -13.4974921 0.0000000
8-5 -12.3592942 -17.7166109 -7.0019775 0.0000000
9-5 0.6291654 -3.7050360 4.9633668 0.9999527
7-6 -11.6648233 -16.4503709 -6.8792758 0.0000000
8-6 -6.0885339 -11.7370427 -0.4400252 0.0237362
9-6 6.8999256 2.2105642 11.5892871 0.0002042
8-7 5.5762894 0.4459943 10.7065845 0.0216716
9-7 18.5647490 14.5145160 22.6149820 0.0000000
9-8 12.9884596 7.9477679 18.0291513 0.0000000

NULL

Week 6

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 14704.30 8 1838.04 37.85 < .001
Residuals 20201.26 416 48.56
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(8, 416) = 37.85, p < .001; Eta2 = 0.42, 95% CI [0.36, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 3.0396538 -0.8288704 6.9081780 0.2599408
3-1 5.6059359 0.9857508 10.2261210 0.0055074
4-1 7.9345270 3.5667539 12.3023000 0.0000010
5-1 9.9104184 5.3949987 14.4258382 0.0000000
6-1 5.1792367 0.2990695 10.0594039 0.0279869
7-1 -7.9437235 -12.1678232 -3.7196237 0.0000003
8-1 -1.7199713 -6.4184099 2.9784673 0.9674920
9-1 11.4551347 7.3480098 15.5622595 0.0000000
3-2 2.5662821 -1.8088627 6.9414269 0.6628087
4-2 4.8948732 0.7871702 9.0025761 0.0070550
5-2 6.8707646 2.6064009 11.1351283 0.0000264
6-2 2.1395829 -2.5092679 6.7884337 0.8836844
7-2 -10.9833773 -14.9379696 -7.0287849 0.0000000
8-2 -4.7596251 -9.2173271 -0.3019231 0.0262610
9-2 8.4154808 4.5860871 12.2448746 0.0000000
4-3 2.3285911 -2.4936339 7.1508161 0.8525849
5-3 4.3044825 -0.6518698 9.2608348 0.1480962
6-3 -0.4266992 -5.7174878 4.8640894 0.9999995
7-3 -13.5496594 -18.2421461 -8.8571726 0.0000000
8-3 -7.3259072 -12.4495522 -2.2022622 0.0003610
9-3 5.8491988 1.2617281 10.4366694 0.0026550
5-4 1.9758914 -2.7460530 6.6978359 0.9296484
6-4 -2.7552903 -7.8271514 2.3165709 0.7500831
7-4 -15.8782504 -20.3224335 -11.4340674 0.0000000
8-4 -9.6544983 -14.5517494 -4.7572471 0.0000001
9-4 3.5206077 -0.8125457 7.8537611 0.2190530
6-5 -4.7311817 -9.9307346 0.4683711 0.1077939
7-5 -17.8541419 -22.4435140 -13.2647697 0.0000000
8-5 -11.6303897 -16.6597677 -6.6010117 0.0000000
9-5 1.5447162 -2.9372245 6.0266570 0.9775410
7-6 -13.1229602 -18.0716320 -8.1742883 0.0000000
8-6 -6.8992080 -12.2584672 -1.5399488 0.0022848
9-6 6.2758980 1.4266909 11.1251050 0.0020998
8-7 6.2237522 1.4541981 10.9933063 0.0018396
9-7 19.3988581 15.2105654 23.5871508 0.0000000
9-8 13.1751059 8.5088331 17.8413788 0.0000000

NULL

Week 7

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 13588.29 8 1698.54 34.85 < .001
Residuals 20325.59 417 48.74
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(8, 417) = 34.85, p < .001; Eta2 = 0.40, 95% CI [0.34, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 3.3580735 -0.5176316 7.2337786 0.1503388
3-1 5.9399863 1.3112251 10.5687475 0.0024010
4-1 8.2089811 3.8331005 12.5848618 0.0000004
5-1 10.1550979 5.6312965 14.6788994 0.0000000
6-1 5.2564617 0.3672358 10.1456876 0.0244005
7-1 -7.0264696 -11.2584102 -2.7945289 0.0000124
8-1 1.0840395 -3.5829968 5.7510757 0.9984352
9-1 12.0366852 7.9219366 16.1514338 0.0000000
3-2 2.5819128 -1.8013533 6.9651789 0.6575928
4-2 4.8509076 0.7355798 8.9662354 0.0081518
5-2 6.7970244 2.5247451 11.0693038 0.0000359
6-2 1.8983882 -2.7590920 6.5558683 0.9392081
7-2 -10.3845431 -14.3464761 -6.4226101 0.0000000
8-2 -2.2740341 -6.6976998 2.1496317 0.8029714
9-2 8.6786117 4.8421097 12.5151137 0.0000000
4-3 2.2689948 -2.5621814 7.1001710 0.8712867
5-3 4.2151116 -0.7504408 9.1806641 0.1707115
6-3 -0.6835246 -5.9841342 4.6170849 0.9999811
7-3 -12.9664559 -17.6676529 -8.2652588 0.0000000
8-3 -4.8559468 -9.9523337 0.2404400 0.0757432
9-3 6.0966989 1.5007128 10.6926850 0.0014021
5-4 1.9461168 -2.7845927 6.6768263 0.9359874
6-4 -2.9525194 -8.0337952 2.1287563 0.6743579
7-4 -15.2354507 -19.6878831 -10.7830182 0.0000000
8-4 -7.1249417 -11.9928016 -2.2570817 0.0002265
9-4 3.8277041 -0.5134927 8.1689009 0.1341496
6-5 -4.8986363 -10.1078407 0.3105681 0.0841124
7-5 -17.1815675 -21.7794586 -12.5836764 0.0000000
8-5 -9.0710585 -14.0723091 -4.0698079 0.0000010
9-5 1.8815872 -2.6086730 6.3718475 0.9291091
7-6 -12.2829312 -17.2407890 -7.3250735 0.0000000
8-6 -4.1724222 -9.5064881 1.1616436 0.2656625
9-6 6.7802235 1.9220152 11.6384318 0.0005719
8-7 8.1105090 3.3716220 12.8493961 0.0000055
9-7 19.0631548 14.8670876 23.2592219 0.0000000
9-8 10.9526457 6.3181139 15.5871775 0.0000000

NULL

Week 8

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 12626.16 8 1578.27 34.52 < .001
Residuals 18929.53 414 45.72
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(8, 414) = 34.52, p < .001; Eta2 = 0.40, 95% CI [0.34, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 3.0546879 -0.6992193 6.8085951 0.2172327
3-1 6.4928132 1.9335804 11.0520460 0.0003905
4-1 8.3427710 4.1044067 12.5811353 0.0000001
5-1 9.3744503 4.9928138 13.7560868 0.0000000
6-1 5.6876364 0.9018493 10.4734235 0.0073145
7-1 -6.7066213 -10.8055691 -2.6076736 0.0000181
8-1 1.8263438 -2.6940262 6.3467138 0.9421330
9-1 11.6880541 7.7026155 15.6734927 0.0000000
3-2 3.4381253 -0.8875035 7.7637542 0.2455252
4-2 5.2880832 1.3020836 9.2740827 0.0014005
5-2 6.3197624 2.1817437 10.4577812 0.0000919
6-2 2.6329485 -1.9308458 7.1967428 0.6829155
7-2 -9.7613092 -13.5987346 -5.9238838 0.0000000
8-2 -1.2283441 -5.5129917 3.0563036 0.9932178
9-2 8.6333662 4.9174301 12.3493024 0.0000000
4-3 1.8499578 -2.9021972 6.6021128 0.9532207
5-3 2.8816371 -1.9987301 7.7620043 0.6545483
6-3 -0.8051768 -6.0514139 4.4410602 0.9999278
7-3 -13.1994345 -17.8276759 -8.5711932 0.0000000
8-3 -4.6664694 -9.6717656 0.3388268 0.0899318
9-3 5.1952409 0.6672208 9.7232610 0.0115080
5-4 1.0316793 -3.5503630 5.6137216 0.9987483
6-4 -2.6551347 -7.6250573 2.3147880 0.7669725
7-4 -15.0493924 -19.3619028 -10.7368820 0.0000000
8-4 -6.5164272 -11.2313098 -1.8015446 0.0006846
9-4 3.3452830 -0.8594873 7.5500534 0.2443358
6-5 -3.6868139 -8.7794692 1.4058413 0.3707210
7-5 -16.0810716 -20.5344695 -11.6276738 0.0000000
8-5 -7.5481065 -12.3921879 -2.7040251 0.0000585
9-5 2.3136038 -2.0355456 6.6627532 0.7711615
7-6 -12.3942577 -17.2458319 -7.5426835 0.0000000
8-6 -3.8612925 -9.0737914 1.3512063 0.3384645
9-6 6.0004177 1.2443563 10.7564791 0.0031223
8-7 8.5329652 3.9430023 13.1229280 0.0000005
9-7 18.3946754 14.3304738 22.4588770 0.0000000
9-8 9.8617103 5.3728232 14.3505973 0.0000000

NULL

Week 9

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 11792.01 7 1684.57 39.07 < .001
Residuals 16297.97 378 43.12
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(7, 378) = 39.07, p < .001; Eta2 = 0.42, 95% CI [0.35, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 3.1172034 -0.4459342 6.680341 0.1362526
4-1 8.4669906 4.4440157 12.489966 0.0000000
5-1 9.9324155 5.7734492 14.091382 0.0000000
6-1 6.2478009 1.7052225 10.790379 0.0008945
7-1 -6.2023255 -10.1339517 -2.270699 0.0000598
8-1 2.7329487 -1.5577008 7.023598 0.5232263
9-1 11.5723402 7.7894375 15.355243 0.0000000
4-2 5.3497873 1.5663521 9.133223 0.0005480
5-2 6.8152121 2.8874832 10.742941 0.0000058
6-2 3.1305975 -1.2012695 7.462465 0.3523943
7-2 -9.3195288 -13.0056838 -5.633374 0.0000000
8-2 -0.3842547 -4.4511610 3.682652 0.9999919
9-2 8.4551369 4.9280408 11.982233 0.0000000
5-4 1.4654248 -2.8837628 5.814612 0.9701504
6-4 -2.2191898 -6.9365461 2.498166 0.8410305
7-4 -14.6693161 -18.8016420 -10.536990 0.0000000
8-4 -5.7340420 -10.2093191 -1.258765 0.0027927
9-4 3.1053496 -0.8857387 7.096438 0.2584822
6-5 -3.6846146 -8.5184664 1.149237 0.2836228
7-5 -16.1347409 -20.3995727 -11.869909 0.0000000
8-5 -7.1994668 -11.7973770 -2.601557 0.0000706
9-5 1.6399248 -2.4882053 5.768055 0.9284689
7-6 -12.4501263 -17.0898254 -7.810427 0.0000000
8-6 -3.5148522 -8.4624572 1.432753 0.3752894
9-6 5.3245394 0.8101761 9.838903 0.0087125
8-7 8.9352742 4.5419312 13.328617 0.0000000
9-7 17.7746657 13.8756732 21.673658 0.0000000
9-8 8.8393916 4.5786251 13.100158 0.0000000

NULL

Week 10

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 7389.02 6 1231.50 27.63 < .001
Residuals 14218.88 319 44.57
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(6, 319) = 27.63, p < .001; Eta2 = 0.34, 95% CI [0.27, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 3.0526806 -0.4742024 6.5795636 0.1393582
4-1 8.4287083 4.4466666 12.4107500 0.0000000
5-1 9.1425096 5.0258604 13.2591589 0.0000000
6-1 6.8534719 2.3571137 11.3498300 0.0001736
7-1 -5.8653066 -9.8234710 -1.9071422 0.0002993
8-1 2.8606948 -1.3862978 7.1076874 0.4175103
4-2 5.3760277 1.6310885 9.1209669 0.0005336
5-2 6.0898290 2.2020643 9.9775937 0.0000997
6-2 3.8007913 -0.4869995 8.0885820 0.1204344
7-2 -8.9179872 -12.6375274 -5.1984471 0.0000000
8-2 -0.1919858 -4.2175118 3.8335402 0.9999993
5-4 0.7138013 -3.5911338 5.0187365 0.9989461
6-4 -1.5752364 -6.2445942 3.0941213 0.9535304
7-4 -14.2940149 -18.4476556 -10.1403742 0.0000000
8-4 -5.5680135 -9.9977552 -1.1382717 0.0042188
6-5 -2.2890378 -7.0737057 2.4956301 0.7910005
7-5 -15.0078162 -19.2906746 -10.7249578 0.0000000
8-5 -6.2818148 -10.8329418 -1.7306878 0.0010408
7-6 -12.7187785 -17.3677903 -8.0697666 0.0000000
8-6 -3.9927770 -8.8900408 0.9044867 0.1939293
8-7 8.7260014 4.3177114 13.1342915 0.0000002

NULL

Week 11

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 5404.25 5 1080.85 23.11 < .001
Residuals 13422.65 287 46.77
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(5, 287) = 23.11, p < .001; Eta2 = 0.29, 95% CI [0.21, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 2.6244016 -0.8687566 6.1175599 0.2621458
4-1 7.3495216 3.4055570 11.2934863 0.0000027
5-1 8.1265423 4.0492572 12.2038273 0.0000004
7-1 -5.4011882 -9.3952803 -1.4070960 0.0017906
8-1 3.6952774 -0.5111047 7.9016594 0.1215739
4-2 4.7251200 1.0159907 8.4342493 0.0041065
5-2 5.5021407 1.6515515 9.3527298 0.0007601
7-2 -8.0255898 -11.7879767 -4.2632029 0.0000000
8-2 1.0708757 -2.9161574 5.0579089 0.9722371
5-4 0.7770207 -3.4867499 5.0407912 0.9952430
7-4 -12.7507098 -16.9349968 -8.5664228 0.0000000
8-4 -3.6542443 -8.0416280 0.7331394 0.1633060
7-5 -13.5277305 -17.8379107 -9.2175502 0.0000000
8-5 -4.4312649 -8.9388732 0.0763433 0.0570822
8-7 9.0964655 4.6639662 13.5289649 0.0000002

NULL

Week 12

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 5308.19 5 1061.64 24.60 < .001
Residuals 12212.13 283 43.15
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(5, 283) = 24.60, p < .001; Eta2 = 0.30, 95% CI [0.23, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 2.5850866 -0.7706211 5.9407944 0.2361785
4-1 7.9012725 4.1124970 11.6900480 0.0000001
5-1 7.7964830 3.8796330 11.7133330 0.0000004
7-1 -5.4260186 -9.3718340 -1.4802033 0.0013955
8-1 4.1486809 0.1078137 8.1895481 0.0403625
4-2 5.3161859 1.7530052 8.8793665 0.0003643
5-2 5.2113964 1.5123221 8.9104706 0.0009571
7-2 -8.0111053 -11.7408365 -4.2813741 0.0000000
8-2 1.5635943 -2.2665551 5.3937436 0.8501596
5-4 -0.1047895 -4.2007871 3.9912080 0.9999997
7-4 -13.3272912 -17.4509959 -9.2035864 0.0000000
8-4 -3.7525916 -7.9673383 0.4621551 0.1121841
7-5 -13.2225016 -17.4641799 -8.9808234 0.0000000
8-5 -3.6478021 -7.9780427 0.6824385 0.1538091
8-7 9.5746996 5.2182413 13.9311578 0.0000000

NULL

Week 13

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 4585.50 5 917.10 21.15 < .001
Residuals 12183.21 281 43.36
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(5, 281) = 21.15, p < .001; Eta2 = 0.27, 95% CI [0.20, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 3.3774645 0.0136611 6.7412679 0.0484177
4-1 8.1710155 4.3730996 11.9689315 0.0000000
5-1 7.6982301 3.7719307 11.6245295 0.0000007
7-1 -4.1289287 -8.1463249 -0.1115326 0.0400092
8-1 4.9892479 0.9386321 9.0398637 0.0062980
4-2 4.7935510 1.2217742 8.3653278 0.0020048
5-2 4.3207656 0.6127673 8.0287639 0.0119677
7-2 -7.5063933 -11.3107192 -3.7020673 0.0000006
8-2 1.6117834 -2.2276062 5.4511730 0.8344079
5-4 -0.4727854 -4.5786645 3.6330938 0.9994745
7-4 -12.2999443 -16.4930205 -8.1068680 0.0000000
8-4 -3.1817676 -7.4066824 1.0431472 0.2595433
7-5 -11.8271589 -16.1368630 -7.5174547 0.0000000
8-5 -2.7089822 -7.0496695 1.6317051 0.4733373
8-7 9.1181766 4.6949187 13.5414346 0.0000001

NULL

Week 14

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 4159.97 5 831.99 18.45 < .001
Residuals 12672.91 281 45.10
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(5, 281) = 18.45, p < .001; Eta2 = 0.25, 95% CI [0.17, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 3.141740 -0.2890009 6.5724806 0.0940028
4-1 7.861652 3.9881604 11.7351441 0.0000002
5-1 6.856717 2.8522870 10.8611470 0.0000224
7-1 -3.849219 -7.9465589 0.2481202 0.0792675
8-1 5.811176 1.6799556 9.9423961 0.0009820
4-2 4.719912 1.0770596 8.3627651 0.0032826
5-2 3.714977 -0.0668077 7.4967620 0.0574025
7-2 -6.990959 -10.8709886 -3.1109298 0.0000066
8-2 2.669436 -1.2463548 6.5852268 0.3704026
5-4 -1.004935 -5.1925185 3.1826481 0.9831156
7-4 -11.710872 -15.9873871 -7.4343560 0.0000000
8-4 -2.050476 -6.3594640 2.2585113 0.7476305
7-5 -10.705936 -15.1014006 -6.3104721 0.0000000
8-5 -1.045541 -5.4726051 3.3815228 0.9842862
8-7 9.660395 5.1491175 14.1716730 0.0000000

NULL

Week 15

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 1718.28 4 429.57 9.60 < .001
Residuals 10965.29 245 44.76
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(4, 245) = 9.60, p < .001; Eta2 = 0.14, 95% CI [0.07, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 2.6307040 -0.6425016 5.903910 0.1798951
4-1 7.3674144 3.6717881 11.063041 0.0000011
5-1 5.9265812 2.1060292 9.747133 0.0002768
8-1 5.1875960 1.2460759 9.129116 0.0033028
4-2 4.7367103 1.2611326 8.212288 0.0020779
5-2 3.2958771 -0.3122532 6.904007 0.0916338
8-2 2.5568920 -1.1790909 6.292875 0.3304011
5-4 -1.4408332 -5.4361282 2.554462 0.8591905
8-4 -2.1798183 -6.2909430 1.931306 0.5913006
8-5 -0.7389852 -4.9627642 3.484794 0.9890488

NULL

Week 16

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 1842.98 4 460.75 9.87 < .001
Residuals 11441.67 245 46.70
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(4, 245) = 9.87, p < .001; Eta2 = 0.14, 95% CI [0.07, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 2.6584406 -0.6851095 6.001991 0.1888063
4-1 7.4133038 3.6382548 11.188353 0.0000016
5-1 6.3856509 2.4829914 10.288310 0.0001036
8-1 5.4814808 1.4552535 9.507708 0.0021077
4-2 4.7548632 1.2045917 8.305135 0.0026299
5-2 3.7272103 0.0415376 7.412883 0.0459945
8-2 2.8230402 -0.9932327 6.639313 0.2534124
5-4 -1.0276529 -5.1088109 3.053505 0.9580838
8-4 -1.9318230 -6.1312999 2.267654 0.7133818
8-5 -0.9041701 -5.2187224 3.410382 0.9784851

NULL

Week 17

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 1613.15 4 403.29 9.12 < .001
Residuals 10828.06 245 44.20
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(4, 245) = 9.12, p < .001; Eta2 = 0.13, 95% CI [0.06, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 2.7500035 -0.5026557 6.002663 0.1409517
4-1 7.3442407 3.6718127 11.016669 0.0000010
5-1 5.5544634 1.7578938 9.351033 0.0007320
8-1 4.9765052 1.0597268 8.893284 0.0051157
4-2 4.5942373 1.1404764 8.047998 0.0028770
5-2 2.8044599 -0.7810215 6.389941 0.2027516
8-2 2.2265017 -1.4860297 5.939033 0.4681827
5-4 -1.7897774 -5.7599931 2.180438 0.7285893
8-4 -2.3677356 -6.4530539 1.717583 0.5034401
8-5 -0.5779582 -4.7752238 3.619307 0.9956218

NULL

Week 18

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 1476.43 4 369.11 8.49 < .001
Residuals 10651.31 245 43.47
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(4, 245) = 8.49, p < .001; Eta2 = 0.12, 95% CI [0.06, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 2.4858398 -0.7401621 5.711842 0.2158672
4-1 6.9747089 3.3323783 10.617040 0.0000031
5-1 4.7873105 1.0218559 8.552765 0.0050764
8-1 5.3034031 1.4187248 9.188081 0.0020291
4-2 4.4888691 1.0634136 7.914325 0.0034912
5-2 2.3014707 -1.2546257 5.857567 0.3884204
8-2 2.8175634 -0.8645419 6.499669 0.2221118
5-4 -2.1873984 -6.1250761 1.750279 0.5461634
8-4 -1.6713057 -5.7231427 2.380531 0.7885849
8-5 0.5160926 -3.6467742 4.678959 0.9970863

NULL

Week 19

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 440.17 3 146.72 3.16 0.026
Residuals 9336.02 201 46.45
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and small (F(3, 201) = 3.16, p = 0.026; Eta2 = 0.05, 95% CI [3.02e-03, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 2.4311200 -0.7122075 5.574447 0.1900496
5-1 4.1678055 0.4988501 7.836761 0.0189029
8-1 2.8485942 -0.9365295 6.633718 0.2108565
5-2 1.7366855 -1.7282770 5.201648 0.5649719
8-2 0.4174742 -3.1702679 4.005216 0.9904586
8-5 -1.3192113 -5.3753942 2.736972 0.8340526

NULL

Week 20

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 350.37 3 116.79 2.53 0.059
Residuals 9288.26 201 46.21
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically not significant and small (F(3, 201) = 2.53, p = 0.059; Eta2 = 0.04, 95% CI [0.00, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 2.7050189 -0.4302579 5.840296 0.1173461
5-1 3.3804976 -0.2790610 7.040056 0.0816576
8-1 2.7502497 -1.0251796 6.525679 0.2368045
5-2 0.6754786 -2.7806095 4.131567 0.9575067
8-2 0.0452308 -3.5333223 3.623784 0.9999873
8-5 -0.6302479 -4.6760420 3.415546 0.9777188

NULL

Week 21

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 285.27 3 95.09 2.20 0.089
Residuals 8691.82 201 43.24
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically not significant and small (F(3, 201) = 2.20, p = 0.089; Eta2 = 0.03, 95% CI [0.00, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 2.4292731 -0.6036689 5.462215 0.1648439
5-1 2.8371689 -0.7029423 6.377280 0.1644339
8-1 2.8280969 -0.8241030 6.480297 0.1891544
5-2 0.4078958 -2.9353862 3.751178 0.9890394
8-2 0.3988239 -3.0629259 3.860574 0.9907331
8-5 -0.0090719 -3.9228121 3.904668 0.9999999

NULL

Week 22

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 454.98 2 227.49 5.36 0.006
Residuals 6839.21 161 42.48
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(2, 161) = 5.36, p = 0.006; Eta2 = 0.06, 95% CI [0.01, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 2.621362 -0.1235423 5.366265 0.0646433
8-1 4.445097 1.1113285 7.778865 0.0054270
8-2 1.823735 -1.3392189 4.986689 0.3622754

NULL

Week 23

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 592.03 2 296.02 6.51 0.002
Residuals 7230.31 159 45.47
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(2, 159) = 6.51, p = 0.002; Eta2 = 0.08, 95% CI [0.02, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 3.247427 0.4071025 6.087752 0.0205897
8-1 5.004214 1.4912529 8.517176 0.0026992
8-2 1.756787 -1.5827743 5.096348 0.4288207

NULL

Week 24

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 698.79 2 349.39 7.61 < .001
Residuals 7298.07 159 45.90
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(2, 159) = 7.61, p < .001; Eta2 = 0.09, 95% CI [0.03, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 3.189458 0.3358549 6.043062 0.0243150
8-1 5.621529 2.0921443 9.150913 0.0006736
8-2 2.432071 -0.9231033 5.787244 0.2027588

NULL

Week 25

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 479.66 2 239.83 6.09 0.003
Residuals 6260.81 159 39.38
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(2, 159) = 6.09, p = 0.003; Eta2 = 0.07, 95% CI [0.02, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 2.537799 -0.1052487 5.180847 0.0627888
8-1 4.699379 1.4304131 7.968344 0.0024340
8-2 2.161580 -0.9460294 5.269189 0.2296551

NULL

Week 26

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 732.14 2 366.07 9.98 < .001
Residuals 5834.69 159 36.70
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(2, 159) = 9.98, p < .001; Eta2 = 0.11, 95% CI [0.04, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 2.983246 0.4317276 5.534764 0.0173505
8-1 5.855742 2.6999812 9.011502 0.0000609
8-2 2.872496 -0.1274960 5.872488 0.0637389

NULL

Week 27

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 611.53 2 305.76 9.48 < .001
Residuals 5128.41 159 32.25
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(2, 159) = 9.48, p < .001; Eta2 = 0.11, 95% CI [0.04, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 2.791087 0.3989767 5.183197 0.0176324
8-1 5.331819 2.3732167 8.290421 0.0001015
8-2 2.540732 -0.2718333 5.353297 0.0856555

NULL

Week 28

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 601.83 2 300.92 7.67 < .001
Residuals 6238.90 159 39.24
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(2, 159) = 7.67, p < .001; Eta2 = 0.09, 95% CI [0.03, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 3.266623 0.6282056 5.905041 0.0108253
8-1 5.050466 1.7872272 8.313705 0.0009889
8-2 1.783843 -1.3183222 4.886008 0.3641931

NULL

Week 29

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 902.92 2 451.46 12.29 < .001
Residuals 5842.97 159 36.75
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(2, 159) = 12.29, p < .001; Eta2 = 0.13, 95% CI [0.06, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 3.618120 1.0647923 6.171448 0.0028626
8-1 6.393294 3.2352952 9.551292 0.0000113
8-2 2.775174 -0.2269458 5.777293 0.0764988

NULL

Week 30

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 790.30 2 395.15 11.32 < .001
Residuals 5550.74 159 34.91
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(2, 159) = 11.32, p < .001; Eta2 = 0.12, 95% CI [0.05, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 3.545213 1.0565552 6.033871 0.0026981
8-1 5.904671 2.8266571 8.982685 0.0000330
8-2 2.359458 -0.5666251 5.285541 0.1398156

NULL

Week 31

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 599.56 2 299.78 8.37 < .001
Residuals 5695.93 159 35.82
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(2, 159) = 8.37, p < .001; Eta2 = 0.10, 95% CI [0.03, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 2.905607 0.3846111 5.426603 0.0193770
8-1 5.227656 2.1096460 8.345666 0.0003226
8-2 2.322049 -0.6420557 5.286154 0.1558365

NULL

Week 32

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 514.19 2 257.09 6.65 0.002
Residuals 6145.48 159 38.65
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(2, 159) = 6.65, p = 0.002; Eta2 = 0.08, 95% CI [0.02, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 3.176745 0.5581539 5.795337 0.0128761
8-1 4.551938 1.3132205 7.790656 0.0031331
8-2 1.375193 -1.7036614 4.454047 0.5423411

NULL

Week 33

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 767.98 2 383.99 10.67 < .001
Residuals 5724.21 159 36.00
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(2, 159) = 10.67, p < .001; Eta2 = 0.12, 95% CI [0.05, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 3.581162 1.0539173 6.108407 0.0028626
8-1 5.772860 2.6471208 8.898599 0.0000663
8-2 2.191698 -0.7797548 5.163150 0.1918014

NULL

Week 34

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 989.37 2 494.69 12.38 < .001
Residuals 6314.03 158 39.96
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(2, 158) = 12.38, p < .001; Eta2 = 0.14, 95% CI [0.06, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 4.308961 1.638395 6.979528 0.0005637
8-1 6.395990 3.102593 9.689388 0.0000261
8-2 2.087029 -1.050413 5.224471 0.2599581

NULL

Week 35

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 834.68 2 417.34 9.79 < .001
Residuals 6736.35 158 42.64
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(2, 158) = 9.79, p < .001; Eta2 = 0.11, 95% CI [0.04, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 3.944952 1.186519 6.703386 0.0025833
8-1 5.884152 2.482394 9.285910 0.0001996
8-2 1.939200 -1.301471 5.179870 0.3352120

NULL

Week 36

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 854.78 2 427.39 9.59 < .001
Residuals 6998.03 157 44.57
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(2, 157) = 9.59, p < .001; Eta2 = 0.11, 95% CI [0.04, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 3.395902 0.5604359 6.231368 0.0143255
8-1 6.307131 2.8166386 9.797623 0.0000974
8-2 2.911229 -0.4024883 6.224946 0.0975533

NULL

Week 37

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 843.12 1 843.12 16.28 < .001
Residuals 4349.28 84 51.78
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(1, 84) = 16.28, p < .001; Eta2 = 0.16, 95% CI [0.06, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
8-1 6.438708 3.265687 9.611729 0.0001198

NULL

Week 38

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 966.36 1 966.36 17.47 < .001
Residuals 4590.70 83 55.31
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(1, 83) = 17.47, p < .001; Eta2 = 0.17, 95% CI [0.07, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
8-1 6.918628 3.626494 10.21076 7.18e-05

NULL

Week 39

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 881.83 1 881.83 16.28 < .001
Residuals 4387.66 81 54.17
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(1, 81) = 16.28, p < .001; Eta2 = 0.17, 95% CI [0.06, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
8-1 6.696864 3.394404 9.999325 0.0001231

NULL

Week 40

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 1079.68 1 1079.68 20.13 < .001
Residuals 4291.12 80 53.64
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(1, 80) = 20.13, p < .001; Eta2 = 0.20, 95% CI [0.09, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
8-1 7.438654 4.139108 10.7382 2.4e-05

NULL

Week 41

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 759.58 1 759.58 12.15 < .001
Residuals 3937.36 63 62.50
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(1, 63) = 12.15, p < .001; Eta2 = 0.16, 95% CI [0.05, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
8-1 6.83773 2.918268 10.75719 0.0008981

NULL

Week 42

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 604.37 1 604.37 9.62 0.003
Residuals 3770.81 60 62.85
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(1, 60) = 9.62, p = 0.003; Eta2 = 0.14, 95% CI [0.03, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
8-1 6.247564 2.217644 10.27748 0.0029368

NULL

Week 43

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 472.47 1 472.47 8.28 0.006
Residuals 3195.64 56 57.07
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(1, 56) = 8.28, p = 0.006; Eta2 = 0.13, 95% CI [0.02, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
8-1 5.721866 1.738323 9.705409 0.0056649

NULL

Model fitting for DMI (kg) as output variable

Week 1

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 1555.18 6 259.20 22.11 < .001
Residuals 3963.20 338 11.73
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(6, 338) = 22.11, p < .001; Eta2 = 0.28, 95% CI [0.21, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 0.4386388 -1.3696248 2.2469024 0.9913566
3-1 -2.7939389 -4.9535512 -0.6343265 0.0028059
5-1 1.1184541 -0.9921878 3.2290960 0.7004593
6-1 -2.2641282 -4.5452639 0.0170075 0.0532000
7-1 -5.4004388 -7.3749090 -3.4259686 0.0000000
9-1 -0.4244932 -2.3442859 1.4952994 0.9947599
3-2 -3.2325777 -5.2776509 -1.1875045 0.0000813
5-2 0.6798153 -1.3134755 2.6731061 0.9511589
6-2 -2.7027670 -4.8757785 -0.5297555 0.0048460
7-2 -5.8390776 -7.6875721 -3.9905832 0.0000000
9-2 -0.8631320 -2.6531049 0.9268408 0.7850805
5-3 3.9123930 1.5956460 6.2291399 0.0000182
6-3 0.5298107 -1.9432617 3.0028831 0.9955923
7-3 -2.6064999 -4.7999082 -0.4130917 0.0086611
9-3 2.3694456 0.2251250 4.5137663 0.0196434
6-5 -3.3825823 -5.8130084 -0.9521562 0.0009017
7-5 -6.5188929 -8.6641023 -4.3736834 0.0000000
9-5 -1.5429473 -3.6379401 0.5520455 0.3066354
7-6 -3.1363106 -5.4494675 -0.8231538 0.0013794
9-6 1.8396349 -0.4270291 4.1062990 0.1986389
9-7 4.9759456 3.0182126 6.9336785 0.0000000

NULL

Week 2

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 2388.08 6 398.01 31.71 < .001
Residuals 4242.40 338 12.55
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(6, 338) = 31.71, p < .001; Eta2 = 0.36, 95% CI [0.29, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 0.1651730 -1.7057019 2.0360480 0.9999732
3-1 -1.1532886 -3.3876778 1.0811006 0.7259378
5-1 2.5918899 0.4081668 4.7756131 0.0087876
6-1 -0.0457822 -2.4059025 2.3143382 1.0000000
7-1 -6.5120531 -8.5548896 -4.4692166 0.0000000
9-1 0.8150945 -1.1711712 2.8013603 0.8872075
3-2 -1.3184617 -3.4343458 0.7974225 0.5163292
5-2 2.4267169 0.3644081 4.4890256 0.0097483
6-2 -0.2109552 -2.4592075 2.0372972 0.9999616
7-2 -6.6772261 -8.5897249 -4.7647273 0.0000000
9-2 0.6499215 -1.2020295 2.5018724 0.9440971
5-3 3.7451785 1.3482140 6.1421431 0.0001037
6-3 1.1075065 -1.4511964 3.6662094 0.8590427
7-3 -5.3587645 -7.6281198 -3.0894092 0.0000000
9-3 1.9683831 -0.2501849 4.1869511 0.1198178
6-5 -2.6376721 -5.1522520 -0.1230922 0.0327966
7-5 -9.1039430 -11.3234306 -6.8844554 0.0000000
9-5 -1.7767954 -3.9443276 0.3907368 0.1887447
7-6 -6.4662709 -8.8595211 -4.0730207 0.0000000
9-6 0.8608767 -1.4842709 3.2060242 0.9311307
9-7 7.3271476 5.3016279 9.3526673 0.0000000

NULL

Week 3

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 3158.08 8 394.76 34.78 < .001
Residuals 4358.26 384 11.35
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(8, 384) = 34.78, p < .001; Eta2 = 0.42, 95% CI [0.35, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 0.4951625 -1.3758876 2.3662126 0.9960576
3-1 0.2237412 -2.0108572 2.4583395 0.9999973
4-1 4.4715469 2.3590302 6.5840636 0.0000000
5-1 3.0300447 0.8461172 5.2139722 0.0006436
6-1 0.6243400 -1.7360012 2.9846812 0.9960711
7-1 -5.8669773 -7.9100050 -3.8239496 0.0000000
8-1 -2.8652660 -9.0962951 3.3657632 0.8838760
9-1 1.6484411 -0.3380105 3.6348928 0.1947262
3-2 -0.2714214 -2.3875035 1.8446608 0.9999817
4-2 3.9763844 1.9896531 5.9631156 0.0000000
5-2 2.5348822 0.4723804 4.5973839 0.0046110
6-2 0.1291775 -2.1192853 2.3776402 1.0000000
7-2 -6.3621398 -8.2748176 -4.4494620 0.0000000
8-2 -3.3604285 -9.5499437 2.8290867 0.7502142
9-2 1.1532786 -0.6988456 3.0054029 0.5845749
4-3 4.2478057 1.9154888 6.5801227 0.0000009
5-3 2.8063035 0.4091146 5.2034925 0.0089418
6-3 0.4005988 -2.1583435 2.9595412 0.9999156
7-3 -6.0907185 -8.3602862 -3.8211508 0.0000000
8-3 -3.0890071 -9.3979452 3.2199310 0.8421224
9-3 1.4247000 -0.7940757 3.6434757 0.5421283
5-4 -1.4415022 -3.7253175 0.8423130 0.5660113
6-4 -3.8472069 -6.3002628 -1.3941510 0.0000512
7-4 -10.3385242 -12.4879974 -8.1890511 0.0000000
8-4 -7.3368129 -13.6035502 -1.0700755 0.0089328
9-4 -2.8231057 -4.9188784 -0.7273331 0.0010880
6-5 -2.4057047 -4.9205200 0.1091106 0.0733018
7-5 -8.8970220 -11.1167173 -6.6773267 0.0000000
8-5 -5.8953106 -12.1864798 0.3958585 0.0864065
9-5 -1.3816035 -3.5493386 0.7861315 0.5525327
7-6 -6.4913173 -8.8847915 -4.0978431 0.0000000
8-6 -3.4896060 -9.8441697 2.8649578 0.7382417
9-6 1.0241012 -1.3212659 3.3694682 0.9111818
8-7 3.0017114 -3.2419440 9.2453667 0.8553170
9-7 7.5154185 5.4897092 9.5411278 0.0000000
9-8 4.5137071 -1.7116652 10.7390794 0.3681595

NULL

Week 4

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 3621.69 8 452.71 41.19 < .001
Residuals 4418.39 402 10.99
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(8, 402) = 41.19, p < .001; Eta2 = 0.45, 95% CI [0.39, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 0.9198041 -0.9209764 2.7605846 0.8266208
3-1 1.3264039 -0.8720435 3.5248512 0.6268353
4-1 5.8892756 3.8109349 7.9676164 0.0000000
5-1 3.3904937 1.2418974 5.5390899 0.0000439
6-1 1.1204297 -1.2017263 3.4425856 0.8530036
7-1 -5.2450386 -7.2550145 -3.2350626 0.0000000
8-1 -1.7789716 -4.4312564 0.8733132 0.4803515
9-1 2.3777621 0.4234469 4.3320772 0.0053093
3-2 0.4065997 -1.6752488 2.4884482 0.9995565
4-2 4.9694715 3.0148813 6.9240617 0.0000000
5-2 2.4706895 0.4415546 4.4998244 0.0052523
6-2 0.2006255 -2.0114620 2.4127130 0.9999988
7-2 -6.1648427 -8.0465775 -4.2831079 0.0000000
8-2 -2.6987757 -5.2552454 -0.1423060 0.0295795
9-2 1.4579579 -0.3642029 3.2801188 0.2372693
4-3 4.5628718 2.2682867 6.8574568 0.0000000
5-3 2.0640898 -0.2943178 4.4224974 0.1408223
6-3 -0.2059742 -2.7235184 2.3115700 0.9999995
7-3 -6.5714424 -8.8042934 -4.3385914 0.0000000
8-3 -3.1053755 -5.9303064 -0.2804445 0.0191121
9-3 1.0513582 -1.1315224 3.2342389 0.8542572
5-4 -2.4987819 -4.7456500 -0.2519139 0.0167359
6-4 -4.7688460 -7.1822167 -2.3554752 0.0000001
7-4 -11.1343142 -13.2490135 -9.0196148 0.0000000
8-4 -7.6682472 -10.4007488 -4.9357456 0.0000000
9-4 -3.5115135 -5.5733811 -1.4496460 0.0000064
6-5 -2.2700640 -4.7441950 0.2040670 0.1014109
7-5 -8.6355322 -10.8193177 -6.4517468 0.0000000
8-5 -5.1694653 -7.9557765 -2.3831540 0.0000005
9-5 -1.0127316 -3.1453974 1.1199342 0.8639427
7-6 -6.3654682 -8.7202211 -4.0107153 0.0000000
8-6 -2.8994013 -5.8216389 0.0228364 0.0536621
9-6 1.2573324 -1.0500916 3.5647565 0.7467111
8-7 3.4660670 0.7851963 6.1469377 0.0021395
9-7 7.6228006 5.6298629 9.6157383 0.0000000
9-8 4.1567337 1.5173375 6.7961298 0.0000460

NULL

Week 5

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 3442.66 8 430.33 37.11 < .001
Residuals 4708.51 406 11.60
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(8, 406) = 37.11, p < .001; Eta2 = 0.42, 95% CI [0.36, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 1.4346908 -0.4560767 3.3254583 0.3055102
3-1 2.0159897 -0.2421572 4.2741366 0.1232601
4-1 5.7765166 3.6417378 7.9112954 0.0000000
5-1 3.7020585 1.4951164 5.9090007 0.0000096
6-1 2.6210650 0.2358501 5.0062799 0.0191865
7-1 -4.8250105 -6.8895680 -2.7604530 0.0000000
8-1 -0.7769949 -3.3387294 1.7847397 0.9901084
9-1 2.7680335 0.7606483 4.7754187 0.0007153
3-2 0.5812989 -1.5570829 2.7196807 0.9952721
4-2 4.3418258 2.3341581 6.3494936 0.0000000
5-2 2.2673678 0.1831310 4.3516045 0.0214680
6-2 1.1863742 -1.0857832 3.4585317 0.7887722
7-2 -6.2597013 -8.1925352 -4.3268674 0.0000000
8-2 -2.2116856 -4.6684993 0.2451280 0.1163232
9-2 1.3333427 -0.5382995 3.2049850 0.3935454
4-3 3.7605269 1.4036316 6.1174222 0.0000337
5-3 1.6860689 -0.7363820 4.1085198 0.4269913
6-3 0.6050753 -1.9808336 3.1909843 0.9983481
7-3 -6.8410002 -9.1344850 -4.5475154 0.0000000
8-3 -2.7929845 -5.5425588 -0.0434103 0.0432463
9-3 0.7520438 -1.4901137 2.9942013 0.9810419
5-4 -2.0744581 -4.3823405 0.2334244 0.1175711
6-4 -3.1554516 -5.6343582 -0.6765449 0.0027284
7-4 -10.6015271 -12.7736518 -8.4294024 0.0000000
8-4 -6.5535115 -9.2027023 -3.9043207 0.0000000
9-4 -3.0084831 -5.1263413 -0.8906249 0.0004108
6-5 -1.0809935 -3.6223104 1.4603233 0.9229836
7-5 -8.5270691 -10.7701559 -6.2839822 0.0000000
8-5 -4.4790534 -7.1867323 -1.7713745 0.0000137
9-5 -0.9340250 -3.1246041 1.2565540 0.9219820
7-6 -7.4460755 -9.8647726 -5.0273785 0.0000000
8-6 -3.3980599 -6.2529122 -0.5432076 0.0071705
9-6 0.1469685 -2.2231144 2.5170514 0.9999999
8-7 4.0480156 1.4550773 6.6409540 0.0000562
9-7 7.5930440 5.5459875 9.6401006 0.0000000
9-8 3.5450284 0.9973771 6.0926796 0.0006060

NULL

Week 6

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 3740.21 8 467.53 37.79 < .001
Residuals 5146.15 416 12.37
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(8, 416) = 37.79, p < .001; Eta2 = 0.42, 95% CI [0.36, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 1.5750734 -0.3774550 3.5276017 0.2275336
3-1 2.8086231 0.4767151 5.1405311 0.0061010
4-1 6.0244127 3.8199025 8.2289229 0.0000000
5-1 3.6979196 1.4188890 5.9769503 0.0000222
6-1 3.2431594 0.7800328 5.7062860 0.0015876
7-1 -4.9556088 -7.0876039 -2.8236137 0.0000000
8-1 -0.4219460 -2.7933502 1.9494583 0.9997790
9-1 2.7533232 0.6803678 4.8262785 0.0013728
3-2 1.2335497 -0.9746811 3.4417806 0.7201486
4-2 4.4493393 2.3760922 6.5225865 0.0000000
5-2 2.1228463 -0.0294709 4.2751635 0.0565675
6-2 1.6680860 -0.6782902 4.0144623 0.3966424
7-2 -6.5306822 -8.5266510 -4.5347133 0.0000000
8-2 -1.9970193 -4.2469186 0.2528799 0.1282732
9-2 1.1782498 -0.7545286 3.1110282 0.6135165
4-3 3.2157896 0.7819076 5.6496716 0.0014988
5-3 0.8892966 -1.6122824 3.3908755 0.9728011
6-3 0.4345363 -2.2358399 3.1049125 0.9998873
7-3 -7.7642319 -10.1326321 -5.3958317 0.0000000
8-3 -3.2305691 -5.8165842 -0.6445539 0.0036193
9-3 -0.0552999 -2.3706962 2.2600964 1.0000000
5-4 -2.3264931 -4.7097612 0.0567751 0.0618821
6-4 -2.7812533 -5.3411321 -0.2213745 0.0217642
7-4 -10.9800215 -13.2230974 -8.7369456 0.0000000
8-4 -6.4463587 -8.9181080 -3.9746094 0.0000000
9-4 -3.2710895 -5.4581264 -1.0840526 0.0001444
6-5 -0.4547602 -3.0790878 2.1695673 0.9998188
7-5 -8.6535284 -10.9698845 -6.3371724 0.0000000
8-5 -4.1198656 -6.6583022 -1.5814290 0.0000221
9-5 -0.9445965 -3.2067295 1.3175366 0.9304587
7-6 -8.1987682 -10.6964706 -5.7010658 0.0000000
8-6 -3.6651054 -6.3700402 -0.9601706 0.0009749
9-6 -0.4898362 -2.9373366 1.9576641 0.9994700
8-7 4.5336628 2.1263650 6.9409606 0.0000003
9-7 7.7089320 5.5950094 9.8228545 0.0000000
9-8 3.1752692 0.8200997 5.5304387 0.0010636

NULL

Week 7

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 3741.17 8 467.65 39.05 < .001
Residuals 4993.56 417 11.97
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(8, 417) = 39.05, p < .001; Eta2 = 0.43, 95% CI [0.37, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 1.7824324 -0.1385974 3.7034623 0.0932859
3-1 3.0730803 0.7787910 5.3673695 0.0011898
4-1 6.0606667 3.8917200 8.2296133 0.0000000
5-1 3.4887025 1.2464375 5.7309675 0.0000604
6-1 3.6490999 1.2257088 6.0724910 0.0001250
7-1 -4.7856995 -6.8833010 -2.6880981 0.0000000
8-1 0.2032467 -2.1100139 2.5165074 0.9999991
9-1 3.5006037 1.4610896 5.5401178 0.0000051
3-2 1.2906478 -0.8819595 3.4632551 0.6469505
4-2 4.2782342 2.2384331 6.3180354 0.0000000
5-2 1.7062701 -0.4113256 3.8238657 0.2289151
6-2 1.8666675 -0.4418566 4.1751915 0.2247179
7-2 -6.5681320 -8.5319016 -4.6043624 0.0000000
8-2 -1.5791857 -3.7718174 0.6134461 0.3780824
9-2 1.7181713 -0.1834272 3.6197698 0.1133698
4-3 2.9875864 0.5929682 5.3822046 0.0036891
5-3 0.4156222 -2.0456008 2.8768453 0.9998509
6-3 0.5760196 -2.0512777 3.2033169 0.9989678
7-3 -7.8587798 -10.1889726 -5.5285870 0.0000000
8-3 -2.8698335 -5.3959059 -0.3437612 0.0129870
9-3 0.4275235 -1.8505205 2.7055674 0.9996709
5-4 -2.5719642 -4.9167851 -0.2271433 0.0195776
6-4 -2.4115668 -4.9301492 0.1070156 0.0727725
7-4 -10.8463662 -13.0532565 -8.6394759 0.0000000
8-4 -5.8574199 -8.2702207 -3.4446191 0.0000000
9-4 -2.5600629 -4.7118182 -0.4083077 0.0072053
6-5 0.1603974 -2.4215941 2.7423888 0.9999999
7-5 -8.2744020 -10.5533903 -5.9954138 0.0000000
8-5 -3.2854558 -5.7643729 -0.8065386 0.0014230
9-5 0.0119012 -2.2137388 2.2375412 1.0000000
7-6 -8.4347994 -10.8922085 -5.9773903 0.0000000
8-6 -3.4458531 -6.0897333 -0.8019729 0.0018753
9-6 -0.1484962 -2.5565131 2.2595207 0.9999999
8-7 4.9889463 2.6400721 7.3378205 0.0000000
9-7 8.2863033 6.2064829 10.3661236 0.0000000
9-8 3.2973570 1.0002074 5.5945065 0.0003344

NULL

Week 8

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 3885.48 8 485.68 48.96 < .001
Residuals 4117.15 415 9.92
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(8, 415) = 48.96, p < .001; Eta2 = 0.49, 95% CI [0.43, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 1.2807329 -0.4678349 3.0293007 0.3542639
3-1 3.1234714 0.9997833 5.2471594 0.0002052
4-1 5.7128110 3.7385833 7.6870386 0.0000000
5-1 3.0594896 1.0185258 5.1004533 0.0001377
6-1 4.6744480 2.4686189 6.8802771 0.0000000
7-1 -5.2376744 -7.1469619 -3.3283869 0.0000000
8-1 0.2075570 -1.8980287 2.3131428 0.9999977
9-1 2.7042240 0.8478090 4.5606390 0.0002497
3-2 1.8427385 -0.1721370 3.8576139 0.1038711
4-2 4.4320781 2.5754018 6.2887544 0.0000000
5-2 1.7787567 -0.1487301 3.7062434 0.0971730
6-2 3.3937151 1.2924407 5.4949895 0.0000249
7-2 -6.5184073 -8.3058778 -4.7309368 0.0000000
8-2 -1.0731759 -3.0689623 0.9226106 0.7605111
9-2 1.4234911 -0.3073898 3.1543721 0.2050231
4-3 2.5893396 0.3757885 4.8028907 0.0090071
5-3 -0.0639818 -2.3372540 2.2092904 1.0000000
6-3 1.5509766 -0.8714015 3.9733547 0.5466755
7-3 -8.3611458 -10.5169779 -6.2053136 0.0000000
8-3 -2.9159143 -5.2473784 -0.5844502 0.0035586
9-3 -0.4192473 -2.5283965 1.6899019 0.9994959
5-4 -2.6533214 -4.7876341 -0.5190087 0.0038898
6-4 -1.0383630 -3.3308376 1.2541116 0.8927677
7-4 -10.9504854 -12.9592502 -8.9417205 0.0000000
8-4 -5.5052539 -7.7014436 -3.3090643 0.0000000
9-4 -3.0085869 -4.9671666 -1.0500073 0.0000808
6-5 1.6149584 -0.7352326 3.9651494 0.4456218
7-5 -8.2971640 -10.3715541 -6.2227738 0.0000000
8-5 -2.8519326 -5.1083029 -0.5955622 0.0030363
9-5 -0.3552655 -2.3810968 1.6705658 0.9998018
7-6 -9.9121224 -12.1489155 -7.6753292 0.0000000
8-6 -4.4668909 -6.8734146 -2.0603673 0.0000005
9-6 -1.9702239 -4.1620591 0.2216112 0.1175650
8-7 5.4452314 3.3072294 7.5832335 0.0000000
9-7 7.9418984 6.0487957 9.8350012 0.0000000
9-8 2.4966670 0.4057460 4.5875880 0.0068655

NULL

Week 9

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 3444.00 7 492.00 43.32 < .001
Residuals 4292.79 378 11.36
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(7, 378) = 43.32, p < .001; Eta2 = 0.45, 95% CI [0.38, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 1.7840772 -0.0445944 3.6127489 0.0616839
4-1 5.8825397 3.8178705 7.9472089 0.0000000
5-1 3.4552961 1.3208335 5.5897586 0.0000330
6-1 4.3563931 2.0250534 6.6877329 0.0000007
7-1 -4.6822250 -6.7000122 -2.6644378 0.0000000
8-1 0.3625663 -1.8394787 2.5646112 0.9996492
9-1 3.0562560 1.1147966 4.9977154 0.0000627
4-2 4.0984625 2.1567298 6.0401951 0.0000000
5-2 1.6712189 -0.3445682 3.6870059 0.1873015
6-2 2.5723159 0.3491173 4.7955145 0.0110422
7-2 -6.4663022 -8.3581088 -4.5744956 0.0000000
8-2 -1.4215109 -3.5087265 0.6657046 0.4325370
9-2 1.2721788 -0.5379957 3.0823533 0.3898328
5-4 -2.4272436 -4.6593315 -0.1951557 0.0222853
6-4 -1.5261465 -3.9471858 0.8948927 0.5370390
7-4 -10.5647646 -12.6855548 -8.4439744 0.0000000
8-4 -5.5199734 -7.8167729 -3.2231739 0.0000000
9-4 -2.8262837 -4.8745880 -0.7779793 0.0008462
6-5 0.9010971 -1.5797299 3.3819240 0.9550715
7-5 -8.1375210 -10.3263158 -5.9487262 0.0000000
8-5 -3.0927298 -5.4524669 -0.7329927 0.0019751
9-5 -0.3990400 -2.5176769 1.7195968 0.9991503
7-6 -9.0386181 -11.4198021 -6.6574341 0.0000000
8-6 -3.9938269 -6.5330342 -1.4546195 0.0000639
9-6 -1.3001371 -3.6169964 1.0167221 0.6805027
8-7 5.0447912 2.7900420 7.2995405 0.0000000
9-7 7.7384810 5.7374420 9.7395199 0.0000000
9-8 2.6936897 0.5069813 4.8803982 0.0049131

NULL

Week 10

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 3377.74 6 562.96 49.48 < .001
Residuals 3629.66 319 11.38
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(6, 319) = 49.48, p < .001; Eta2 = 0.48, 95% CI [0.42, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 1.6025897 -0.1793428 3.3845222 0.1097490
4-1 5.6238990 3.6120008 7.6357972 0.0000000
5-1 3.4843170 1.4044093 5.5642248 0.0000224
6-1 4.1188292 1.8470763 6.3905822 0.0000030
7-1 -5.1914413 -7.1912756 -3.1916069 0.0000000
8-1 -0.1168303 -2.2625931 2.0289324 0.9999985
4-2 4.0213093 2.1292055 5.9134132 0.0000000
5-2 1.8817274 -0.0825381 3.8459928 0.0703603
6-2 2.5162396 0.3498638 4.6826153 0.0113757
7-2 -6.7940309 -8.6733021 -4.9147598 0.0000000
8-2 -1.7194200 -3.7532884 0.3144483 0.1596780
5-4 -2.1395819 -4.3146198 0.0354559 0.0572127
6-4 -1.5050697 -3.8642295 0.8540900 0.4864515
7-4 -10.8153402 -12.9139376 -8.7167429 0.0000000
8-4 -5.7407293 -7.9788248 -3.5026339 0.0000000
6-5 0.6345122 -1.7829072 3.0519316 0.9868434
7-5 -8.6757583 -10.8396420 -6.5118746 0.0000000
8-5 -3.6011474 -5.9005719 -1.3017229 0.0001001
7-6 -9.3102705 -11.6591507 -6.9613904 0.0000000
8-6 -4.2356596 -6.7099672 -1.7613519 0.0000133
8-7 5.0746109 2.8473538 7.3018681 0.0000000

NULL

Week 11

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 2693.67 5 538.73 44.15 < .001
Residuals 3502.29 287 12.20
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(5, 287) = 44.15, p < .001; Eta2 = 0.43, 95% CI [0.36, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 1.1488052 -0.6355239 2.9331343 0.4371827
4-1 4.9752496 2.9606455 6.9898537 0.0000000
5-1 2.6961552 0.6134501 4.7788603 0.0033161
7-1 -5.4980151 -7.5382247 -3.4578056 0.0000000
8-1 -0.0547278 -2.2033765 2.0939209 0.9999997
4-2 3.8264444 1.9317958 5.7210931 0.0000003
5-2 1.5473500 -0.4195573 3.5142573 0.2153823
7-2 -6.6468203 -8.5686733 -4.7249674 0.0000000
8-2 -1.2035330 -3.2401368 0.8330708 0.5358900
5-4 -2.2790944 -4.4570576 -0.1011313 0.0342957
7-4 -10.4732648 -12.6106272 -8.3359024 0.0000000
8-4 -5.0299775 -7.2710830 -2.7888719 0.0000000
7-5 -8.1941703 -10.3958399 -5.9925008 0.0000000
8-5 -2.7508830 -5.0534001 -0.4483659 0.0090365
8-7 5.4432873 3.1791363 7.7074383 0.0000000

NULL

Week 12

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 2391.68 5 478.34 37.73 < .001
Residuals 3587.92 283 12.68
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(5, 283) = 37.73, p < .001; Eta2 = 0.40, 95% CI [0.32, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 1.2131386 -0.6057666 3.0320439 0.3958593
4-1 4.6983579 2.6447155 6.7520003 0.0000000
5-1 2.7075118 0.5844487 4.8305748 0.0040528
7-1 -5.4298120 -7.5685752 -3.2910488 0.0000000
8-1 0.6123905 -1.5778940 2.8026750 0.9669437
4-2 3.4852192 1.5538567 5.4165817 0.0000063
5-2 1.4943731 -0.5106482 3.4993944 0.2705739
7-2 -6.6429506 -8.6645891 -4.6213122 0.0000000
8-2 -0.6007481 -2.6768165 1.4753203 0.9617013
5-4 -1.9908461 -4.2110131 0.2293209 0.1073938
7-4 -10.1281699 -12.3633550 -7.8929847 0.0000000
8-4 -4.0859673 -6.3705003 -1.8014344 0.0000079
7-5 -8.1373238 -10.4364545 -5.8381930 0.0000000
8-5 -2.0951212 -4.4422557 0.2520133 0.1104475
8-7 6.0422025 3.6808572 8.4035479 0.0000000

NULL

Week 13

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 2136.90 5 427.38 34.48 < .001
Residuals 3482.75 281 12.39
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(5, 281) = 34.48, p < .001; Eta2 = 0.38, 95% CI [0.30, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 1.8202253 0.0217241 3.6187266 0.0454187
4-1 5.0626638 3.0320586 7.0932689 0.0000000
5-1 2.9357366 0.8364895 5.0349836 0.0010767
7-1 -4.5808870 -6.7288401 -2.4329339 0.0000000
8-1 1.0448255 -1.1208889 3.2105399 0.7364483
4-2 3.2424384 1.3327415 5.1521354 0.0000271
5-2 1.1155112 -0.8670183 3.0980407 0.5895927
7-2 -6.4011123 -8.4351447 -4.3670800 0.0000000
8-2 -0.7753998 -2.8281794 1.2773798 0.8875375
5-4 -2.1269272 -4.3221889 0.0683344 0.0636439
7-4 -9.6435508 -11.8854335 -7.4016680 0.0000000
8-4 -4.0178382 -6.2767439 -1.7589326 0.0000091
7-5 -7.5166236 -9.8208629 -5.2123842 0.0000000
8-5 -1.8909110 -4.2117159 0.4298939 0.1824823
8-7 5.6257125 3.2607601 7.9906649 0.0000000

NULL

Week 14

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 2274.11 5 454.82 35.00 < .001
Residuals 3652.07 281 13.00
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(5, 281) = 35.00, p < .001; Eta2 = 0.38, 95% CI [0.31, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 1.8145349 -0.0271657 3.6562356 0.0561208
4-1 5.0294719 2.9500922 7.1088515 0.0000000
5-1 3.4078177 1.2581474 5.5574880 0.0001165
7-1 -4.7488311 -6.9483773 -2.5492848 0.0000000
8-1 1.6484716 -0.5692626 3.8662058 0.2734200
4-2 3.2149369 1.2593697 5.1705042 0.0000551
5-2 1.5932828 -0.4368664 3.6234320 0.2176493
7-2 -6.5633660 -8.6462552 -4.4804769 0.0000000
8-2 -0.1660634 -2.2681500 1.9360233 0.9999175
5-4 -1.6216542 -3.8696453 0.6263370 0.3061123
7-4 -9.7783030 -12.0740350 -7.4825709 0.0000000
8-4 -3.3810003 -5.6941641 -1.0678365 0.0005219
7-5 -8.1566488 -10.5162352 -5.7970623 0.0000000
8-5 -1.7593461 -4.1358960 0.6172038 0.2777916
8-7 6.3973027 3.9755449 8.8190605 0.0000000

NULL

Week 15

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 717.01 4 179.25 14.44 < .001
Residuals 3042.00 245 12.42
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(4, 245) = 14.44, p < .001; Eta2 = 0.19, 95% CI [0.11, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 1.7484598 0.0244375 3.4724821 0.0450075
4-1 4.9859913 3.0394769 6.9325057 0.0000000
5-1 3.3947897 1.3824760 5.4071034 0.0000564
8-1 1.2848383 -0.7911903 3.3608668 0.4353283
4-2 3.2375315 1.4069183 5.0681447 0.0000206
5-2 1.6463299 -0.2540998 3.5467595 0.1240693
8-2 -0.4636215 -2.4313921 1.5041490 0.9669833
5-4 -1.5912017 -3.6955539 0.5131506 0.2329356
8-4 -3.7011531 -5.8665136 -1.5357925 0.0000430
8-5 -2.1099514 -4.3346479 0.1147451 0.0722907

NULL

Week 16

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 659.62 4 164.91 12.57 < .001
Residuals 3213.66 245 13.12
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(4, 245) = 12.57, p < .001; Eta2 = 0.17, 95% CI [0.10, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 1.6941179 -0.0778800 3.4661159 0.0685514
4-1 4.8815354 2.8808538 6.8822169 0.0000000
5-1 2.6914234 0.6231115 4.7597353 0.0038159
8-1 0.9371752 -1.1966246 3.0709750 0.7473321
4-2 3.1874174 1.3058624 5.0689725 0.0000518
5-2 0.9973055 -0.9560088 2.9506198 0.6262262
8-2 -0.7569428 -2.7794719 1.2655864 0.8419806
5-4 -2.1901119 -4.3530236 -0.0272003 0.0455485
8-4 -3.9443602 -6.1699779 -1.7187425 0.0000196
8-5 -1.7542482 -4.0408530 0.5323566 0.2197905

NULL

Week 17

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 610.42 4 152.60 12.10 < .001
Residuals 3089.51 245 12.61
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(4, 245) = 12.10, p < .001; Eta2 = 0.16, 95% CI [0.09, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 1.4325892 -0.3048441 3.1700225 0.1595922
4-1 4.2956943 2.3340382 6.2573505 0.0000001
5-1 3.0850180 1.0570506 5.1129853 0.0003874
8-1 0.3393979 -1.7527800 2.4315757 0.9917826
4-2 2.8631051 1.0182517 4.7079585 0.0002746
5-2 1.6524287 -0.2627842 3.5676416 0.1267937
8-2 -1.0931914 -3.0762691 0.8898863 0.5537376
5-4 -1.2106764 -3.3313982 0.9100454 0.5188128
8-4 -3.9562965 -6.1385012 -1.7740918 0.0000117
8-5 -2.7456201 -4.9876223 -0.5036179 0.0078322

NULL

Week 18

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 542.41 4 135.60 9.59 < .001
Residuals 3462.98 245 14.13
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(4, 245) = 9.59, p < .001; Eta2 = 0.14, 95% CI [0.07, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 1.2857247 -0.5537254 3.1251749 0.3089797
4-1 4.2072503 2.1304116 6.2840890 0.0000007
5-1 2.4461083 0.2990649 4.5931518 0.0165832
8-1 0.3097095 -1.9053146 2.5247337 0.9953551
4-2 2.9215255 0.9683479 4.8747031 0.0005131
5-2 1.1603836 -0.8672849 3.1880520 0.5163397
8-2 -0.9760152 -3.0755332 1.1235028 0.7052571
5-4 -1.7611419 -4.0063861 0.4841022 0.2003107
8-4 -3.8975407 -6.2078779 -1.5872035 0.0000564
8-5 -2.1363988 -4.5100446 0.2372471 0.1000765

NULL

Week 19

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 352.50 3 117.50 7.01 < .001
Residuals 3367.13 201 16.75
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(3, 201) = 7.01, p < .001; Eta2 = 0.09, 95% CI [0.03, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 1.7530965 -0.1346296 3.6408226 0.0792029
5-1 2.0594694 -0.1439228 4.2628615 0.0763012
8-1 -1.4776344 -3.7507914 0.7955226 0.3348049
5-2 0.3063728 -1.7745113 2.3872570 0.9810653
8-2 -3.2307309 -5.3853503 -1.0761115 0.0007969
8-5 -3.5371038 -5.9730454 -1.1011621 0.0012579

NULL

Week 20

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 342.47 3 114.16 7.74 < .001
Residuals 2965.44 201 14.75
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(3, 201) = 7.74, p < .001; Eta2 = 0.10, 95% CI [0.04, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 2.5693202 0.7977711 4.3408692 0.0012786
5-1 2.5169071 0.4491192 4.5846950 0.0099821
8-1 -0.1238442 -2.2571034 2.0094150 0.9987841
5-2 -0.0524130 -2.0052325 1.9004064 0.9998790
8-2 -2.6931643 -4.7151812 -0.6711475 0.0037793
8-5 -2.6407513 -4.9267768 -0.3547258 0.0163157

NULL

Week 21

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 205.78 3 68.59 5.29 0.002
Residuals 2605.91 201 12.96
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(3, 201) = 5.29, p = 0.002; Eta2 = 0.07, 95% CI [0.02, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 1.7139256 0.0532347 3.3746165 0.0401987
5-1 1.1563063 -0.7820857 3.0946984 0.4124364
8-1 -0.9200465 -2.9198129 1.0797199 0.6326743
5-2 -0.5576193 -2.3882373 1.2729988 0.8593337
8-2 -2.6339721 -4.5294573 -0.7384869 0.0022516
8-5 -2.0763528 -4.2193259 0.0666202 0.0613681

NULL

Week 22

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 122.44 2 61.22 4.71 0.010
Residuals 2093.88 161 13.01
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and small (F(2, 161) = 4.71, p = 0.010; Eta2 = 0.06, 95% CI [7.72e-03, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 1.5077599 -0.0110384 3.0265582 0.0521549
8-1 -0.4923205 -2.3369465 1.3523055 0.8030924
8-2 -2.0000804 -3.7501923 -0.2499685 0.0206518

NULL

Week 23

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 247.74 2 123.87 9.03 < .001
Residuals 2180.46 159 13.71
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(2, 159) = 9.03, p < .001; Eta2 = 0.10, 95% CI [0.03, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 2.6592439 1.099462 4.2190261 0.0002500
8-1 0.5573332 -1.371832 2.4864980 0.7734516
8-2 -2.1019107 -3.935852 -0.2679697 0.0202167

NULL

Week 24

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 348.41 2 174.20 13.59 < .001
Residuals 2037.55 159 12.81
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(2, 159) = 13.59, p < .001; Eta2 = 0.15, 95% CI [0.07, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 3.251735 1.7439341 4.7595366 0.0000028
8-1 1.114875 -0.7499989 2.9797488 0.3359255
8-2 -2.136860 -3.9096838 -0.3640369 0.0135861

NULL

Week 25

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 187.60 2 93.80 8.42 < .001
Residuals 1771.28 159 11.14
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(2, 159) = 8.42, p < .001; Eta2 = 0.10, 95% CI [0.03, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 2.0431176 0.6372856 3.4489495 0.0021469
8-1 -0.2865323 -2.0252889 1.4522243 0.9196776
8-2 -2.3296499 -3.9825813 -0.6767185 0.0030385

NULL

Week 26

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 239.96 2 119.98 10.34 < .001
Residuals 1844.93 159 11.60
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(2, 159) = 10.34, p < .001; Eta2 = 0.12, 95% CI [0.04, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 2.6709540 1.2361945 4.1057136 0.0000575
8-1 0.7796968 -0.9948379 2.5542315 0.5531067
8-2 -1.8912572 -3.5782007 -0.2043138 0.0237860

NULL

Week 27

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 194.67 2 97.33 9.92 < .001
Residuals 1560.76 159 9.82
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(2, 159) = 9.92, p < .001; Eta2 = 0.11, 95% CI [0.04, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 2.2693338 0.9496875 3.588980 0.0002185
8-1 0.1950148 -1.4371459 1.827175 0.9569196
8-2 -2.0743190 -3.6259161 -0.522722 0.0052900

NULL

Week 28

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 252.26 2 126.13 11.37 < .001
Residuals 1763.75 159 11.09
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(2, 159) = 11.37, p < .001; Eta2 = 0.13, 95% CI [0.05, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 2.3849702 0.9821283 3.787812 0.0002610
8-1 -0.2962779 -2.0313362 1.438780 0.9140215
8-2 -2.6812481 -4.3306637 -1.031832 0.0005067

NULL

Week 29

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 191.31 2 95.65 9.18 < .001
Residuals 1655.88 159 10.41
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(2, 159) = 9.18, p < .001; Eta2 = 0.10, 95% CI [0.04, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 2.4093057 1.0500389 3.7685726 0.0001341
8-1 0.8246694 -0.8564946 2.5058335 0.4785242
8-2 -1.5846363 -3.1828179 0.0135453 0.0525199

NULL

Week 30

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 267.02 2 133.51 12.26 < .001
Residuals 1731.82 159 10.89
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(2, 159) = 12.26, p < .001; Eta2 = 0.13, 95% CI [0.06, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 2.6988171 1.308733 4.0889014 0.0000262
8-1 0.3580401 -1.361239 2.0773197 0.8748953
8-2 -2.3407771 -3.975193 -0.7063613 0.0025404

NULL

Week 31

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 194.14 2 97.07 10.23 < .001
Residuals 1509.40 159 9.49
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(2, 159) = 10.23, p < .001; Eta2 = 0.11, 95% CI [0.04, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 2.2181551 0.9204022 3.5159080 0.0002403
8-1 0.0555941 -1.5494884 1.6606767 0.9963048
8-2 -2.1625610 -3.6884166 -0.6367055 0.0028569

NULL

Week 32

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 179.33 2 89.66 8.19 < .001
Residuals 1741.05 159 10.95
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(2, 159) = 8.19, p < .001; Eta2 = 0.09, 95% CI [0.03, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 2.0800183 0.6862368 3.4737997 0.0015653
8-1 -0.0833907 -1.8072429 1.6404616 0.9928050
8-2 -2.1634089 -3.8021717 -0.5246462 0.0059919

NULL

Week 33

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 228.91 2 114.45 12.17 < .001
Residuals 1486.24 158 9.41
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(2, 158) = 12.17, p < .001; Eta2 = 0.13, 95% CI [0.06, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 2.6497193 1.3540466 3.9453920 0.0000092
8-1 0.9361127 -0.6617377 2.5339632 0.3505878
8-2 -1.7136065 -3.2357922 -0.1914208 0.0230872

NULL

Week 34

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 168.86 2 84.43 8.10 < .001
Residuals 1647.79 158 10.43
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(2, 158) = 8.10, p < .001; Eta2 = 0.09, 95% CI [0.03, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 2.0817196 0.7174456 3.4459936 0.0011881
8-1 0.0668609 -1.6155901 1.7493118 0.9951379
8-2 -2.0148587 -3.6176388 -0.4120787 0.0094691

NULL

Week 35

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 194.12 2 97.06 8.94 < .001
Residuals 1714.87 158 10.85
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(2, 158) = 8.94, p < .001; Eta2 = 0.10, 95% CI [0.03, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 2.1148860 0.7231223 3.5066496 0.0012520
8-1 -0.2261492 -1.9425009 1.4902026 0.9478557
8-2 -2.3410351 -3.9761106 -0.7059596 0.0025505

NULL

Week 36

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 118.82 2 59.41 4.64 0.011
Residuals 2008.24 157 12.79
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and small (F(2, 157) = 4.64, p = 0.011; Eta2 = 0.06, 95% CI [7.57e-03, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 1.6843299 0.1653771 3.2032828 0.0257321
8-1 -0.1156115 -1.9854604 1.7542374 0.9882707
8-2 -1.7999414 -3.5750923 -0.0247905 0.0460723

NULL

Week 37

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 1.49 1 1.49 0.14 0.706
Residuals 873.80 84 10.40
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically not significant and very small (F(1, 84) = 0.14, p = 0.706; Eta2 = 1.70e-03, 95% CI [0.00, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
8-1 -0.270507 -1.692742 1.151728 0.7062134

NULL

Week 38

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 1.65 1 1.65 0.15 0.697
Residuals 898.27 83 10.82
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically not significant and very small (F(1, 83) = 0.15, p = 0.697; Eta2 = 1.83e-03, 95% CI [0.00, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
8-1 -0.2857376 -1.742008 1.170532 0.6973458

NULL

Week 39

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 10.00 1 10.00 1.08 0.303
Residuals 752.45 81 9.29
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically not significant and small (F(1, 81) = 1.08, p = 0.303; Eta2 = 0.01, 95% CI [0.00, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
8-1 -0.7131589 -2.080764 0.6544464 0.3025658

NULL

Week 40

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 0.04 1 0.04 3.60e-03 0.952
Residuals 827.16 80 10.34
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically not significant and very small (F(1, 80) = 3.60e-03, p = 0.952; Eta2 = 4.50e-05, 95% CI [0.00, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
8-1 0.0436836 -1.404966 1.492333 0.9522975

NULL

Week 41

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 4.02 1 4.02 0.36 0.551
Residuals 705.28 63 11.19
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically not significant and very small (F(1, 63) = 0.36, p = 0.551; Eta2 = 5.67e-03, 95% CI [0.00, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
8-1 -0.4975069 -2.156353 1.16134 0.5511055

NULL

Week 42

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 5.98 1 5.98 0.69 0.411
Residuals 522.96 60 8.72
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically not significant and small (F(1, 60) = 0.69, p = 0.411; Eta2 = 0.01, 95% CI [0.00, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
8-1 -0.6212041 -2.121977 0.8795686 0.4109693

NULL

Week 43

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 10.93 1 10.93 1.14 0.289
Residuals 534.91 56 9.55
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically not significant and small (F(1, 56) = 1.14, p = 0.289; Eta2 = 0.02, 95% CI [0.00, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
8-1 -0.8701893 -2.499976 0.7595969 0.2893938

NULL

Model fitting for FE as output variable

Week 1

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 122.95 6 20.49 25.26 < .001
Residuals 225.56 278 0.81
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(6, 278) = 25.26, p < .001; Eta2 = 0.35, 95% CI [0.27, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 -0.5688962 -1.0451906 -0.0926018 0.0082050
3-1 1.5846680 1.0061941 2.1631418 0.0000000
5-1 -0.0084235 -0.5643640 0.5475169 1.0000000
6-1 0.0671543 -0.5400647 0.6743732 0.9998980
7-1 0.0604691 -1.0897637 1.2107020 0.9999988
9-1 0.6255523 0.0877901 1.1633144 0.0112316
3-2 2.1535642 1.6047299 2.7023984 0.0000000
5-2 0.5604727 0.0354423 1.0855030 0.0278342
6-2 0.6360505 0.0569979 1.2151031 0.0209622
7-2 0.6293653 -0.5062502 1.7649809 0.6524975
9-2 1.1944485 0.6887063 1.7001907 0.0000000
5-3 -1.5930915 -2.2123107 -0.9738722 0.0000000
6-3 -1.5175137 -2.1831544 -0.8518730 0.0000000
7-3 -1.5241988 -2.7063148 -0.3420828 0.0029888
9-3 -0.9591157 -1.5620674 -0.3561639 0.0000746
6-5 0.0755778 -0.5705765 0.7217321 0.9998584
7-5 0.0688927 -1.1023614 1.2401468 0.9999976
9-5 0.6339758 0.0526079 1.2153437 0.0225658
7-6 -0.0066851 -1.2031304 1.1897601 1.0000000
9-6 0.5583980 -0.0721840 1.1889800 0.1210010
9-7 0.5650831 -0.5976526 1.7278189 0.7776320

NULL

Week 2

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 37.78 5 7.56 17.07 < .001
Residuals 124.82 282 0.44
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(5, 282) = 17.07, p < .001; Eta2 = 0.23, 95% CI [0.16, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 -0.0783269 -0.4181943 0.2615404 0.9859297
3-1 0.9866446 0.5738656 1.3994237 0.0000000
5-1 0.0851852 -0.3115149 0.4818852 0.9898017
6-1 0.1257901 -0.3029546 0.5545348 0.9593808
9-1 0.5836536 0.2161985 0.9511088 0.0001119
3-2 1.0649716 0.6733423 1.4566008 0.0000000
5-2 0.1635121 -0.2111315 0.5381557 0.8104095
6-2 0.2041170 -0.2043055 0.6125396 0.7062084
9-2 0.6619806 0.3184544 1.0055068 0.0000011
5-3 -0.9014595 -1.3433131 -0.4596059 0.0000002
6-3 -0.8608546 -1.3316897 -0.3900194 0.0000045
9-3 -0.4029910 -0.8187878 0.0128058 0.0634853
6-5 0.0406049 -0.4161993 0.4974091 0.9998524
9-5 0.4984685 0.0986293 0.8983076 0.0054231
9-6 0.4578636 0.0262127 0.8895144 0.0304208

NULL

Week 3

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 15.11 7 2.16 13.16 < .001
Residuals 54.27 331 0.16
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(7, 331) = 13.16, p < .001; Eta2 = 0.22, 95% CI [0.14, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 -0.0190678 -0.2389671 0.2008315 0.9999955
3-1 0.5489440 0.2863179 0.8115701 0.0000000
4-1 -0.0922294 -0.3405076 0.1560488 0.9491103
5-1 0.0841077 -0.1725632 0.3407786 0.9742932
6-1 0.1363428 -0.1410615 0.4137471 0.8075459
8-1 0.3146542 -0.5744799 1.2037883 0.9607119
9-1 0.3880315 0.1524941 0.6235688 0.0000225
3-2 0.5680118 0.3193146 0.8167090 0.0000000
4-2 -0.0731616 -0.3066566 0.1603334 0.9800617
5-2 0.1031756 -0.1392245 0.3455756 0.8990764
6-2 0.1554107 -0.1088449 0.4196662 0.6246359
8-2 0.3337220 -0.5513979 1.2188420 0.9449932
9-2 0.4070993 0.1872000 0.6269986 0.0000010
4-3 -0.6411734 -0.9152841 -0.3670627 0.0000000
5-3 -0.4648362 -0.7465712 -0.1831013 0.0000217
6-3 -0.4126011 -0.7133465 -0.1118558 0.0009478
8-3 -0.2342897 -1.1309804 0.6624009 0.9932005
9-3 -0.1609125 -0.4235386 0.1017136 0.5732357
5-4 0.1763371 -0.0920733 0.4447476 0.4806941
6-4 0.2285722 -0.0597286 0.5168731 0.2358513
8-4 0.4068836 -0.4857101 1.2994774 0.8612095
9-4 0.4802609 0.2319827 0.7285391 0.0000002
6-5 0.0522351 -0.2433241 0.3477943 0.9994355
8-5 0.2305465 -0.6644181 1.1255110 0.9937647
9-5 0.3039237 0.0472528 0.5605946 0.0083336
8-6 0.1783114 -0.7228183 1.0794411 0.9988199
9-6 0.2516886 -0.0257157 0.5290930 0.1068621
9-8 0.0733772 -0.8157568 0.9625113 0.9999968

NULL

Week 4

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 10.28 7 1.47 7.69 < .001
Residuals 66.27 347 0.19
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(7, 347) = 7.69, p < .001; Eta2 = 0.13, 95% CI [0.07, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 -0.0643986 -0.3016641 0.1728669 0.9914573
3-1 0.4329770 0.1472672 0.7186868 0.0001446
4-1 -0.1097013 -0.3775869 0.1581843 0.9165018
5-1 0.0348237 -0.2421174 0.3117649 0.9999423
6-1 0.1920941 -0.1072179 0.4914060 0.5125531
8-1 0.2688451 -0.0730185 0.6107087 0.2454916
9-1 0.2225824 -0.0339425 0.4791074 0.1433146
3-2 0.4973756 0.2265647 0.7681866 0.0000012
4-2 -0.0453027 -0.2972376 0.2066322 0.9993694
5-2 0.0992223 -0.1623210 0.3607656 0.9433100
6-2 0.2564927 -0.0286321 0.5416175 0.1133687
8-2 0.3332437 0.0037301 0.6627573 0.0452566
9-2 0.2869810 0.0471612 0.5268008 0.0072668
4-3 -0.5426783 -0.8406823 -0.2446744 0.0000015
5-3 -0.3981533 -0.7043233 -0.0919834 0.0022442
6-3 -0.2408829 -0.5674274 0.0856615 0.3249909
8-3 -0.1641319 -0.5300750 0.2018111 0.8712628
9-3 -0.2103946 -0.4982291 0.0774399 0.3369385
5-4 0.1445250 -0.1450828 0.4341328 0.7953147
6-4 0.3017954 -0.0092736 0.6128644 0.0645532
8-4 0.3785464 0.0263434 0.7307494 0.0252026
9-4 0.3322837 0.0621332 0.6024343 0.0050381
6-5 0.1572704 -0.1616302 0.4761710 0.8050801
8-5 0.2340214 -0.1251174 0.5931601 0.4920868
9-5 0.1877587 -0.0913739 0.4668913 0.4489403
8-6 0.0767510 -0.2999079 0.4534099 0.9985778
9-6 0.0304883 -0.2708524 0.3318291 0.9999869
9-8 -0.0462627 -0.3899039 0.2973786 0.9999084

NULL

Week 5

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 12.61 8 1.58 9.00 < .001
Residuals 65.21 372 0.18
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(8, 372) = 9.00, p < .001; Eta2 = 0.16, 95% CI [0.10, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 -0.0746854 -0.3072517 0.1578809 0.9855717
3-1 0.5156827 0.2379283 0.7934370 0.0000005
4-1 -0.0829098 -0.3454897 0.1796701 0.9870670
5-1 0.0339595 -0.2374966 0.3054156 0.9999849
6-1 -0.0367630 -0.3301468 0.2566209 0.9999847
7-1 0.1337154 -0.2139062 0.4813371 0.9561599
8-1 0.0432460 -0.2718499 0.3583419 0.9999691
9-1 0.2557337 0.0066285 0.5048389 0.0390968
3-2 0.5903681 0.3273450 0.8533912 0.0000000
4-2 -0.0082244 -0.2551695 0.2387208 1.0000000
5-2 0.1086449 -0.1477183 0.3650081 0.9242556
6-2 0.0379225 -0.2415552 0.3174001 0.9999717
7-2 0.2084009 -0.1275672 0.5443689 0.5895302
8-2 0.1179314 -0.1842591 0.4201220 0.9523201
9-2 0.3304191 0.0978528 0.5629854 0.0004145
4-3 -0.5985925 -0.8884930 -0.3086920 0.0000000
5-3 -0.4817232 -0.7796871 -0.1837593 0.0000250
6-3 -0.5524456 -0.8705151 -0.2343762 0.0000038
7-3 -0.3819672 -0.7506608 -0.0132737 0.0357725
8-3 -0.4724367 -0.8106371 -0.1342363 0.0005700
9-3 -0.2599490 -0.5377033 0.0178054 0.0872357
5-4 0.1168693 -0.1670026 0.4007412 0.9354094
6-4 0.0461468 -0.2587612 0.3510549 0.9999346
7-4 0.2166252 -0.1407760 0.5740265 0.6203052
8-4 0.1261558 -0.1996973 0.4520089 0.9544749
9-4 0.3386435 0.0760636 0.6012235 0.0022386
6-5 -0.0707225 -0.3833070 0.2418621 0.9986950
7-5 0.0997559 -0.2642164 0.4637283 0.9949734
8-5 0.0092865 -0.3237607 0.3423337 1.0000000
9-5 0.2217742 -0.0496819 0.4932303 0.2122810
7-6 0.1704784 -0.2101284 0.5510852 0.8983695
8-6 0.0800090 -0.2711408 0.4311587 0.9986272
9-6 0.2924967 -0.0008871 0.5858805 0.0513893
8-7 -0.0904695 -0.4880533 0.3071143 0.9986402
9-7 0.1220183 -0.2256034 0.4696400 0.9747076
9-8 0.2124877 -0.1026082 0.5275837 0.4720705

NULL

Week 6

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 3.60 7 0.51 4.24 < .001
Residuals 44.41 366 0.12
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(7, 366) = 4.24, p < .001; Eta2 = 0.07, 95% CI [0.02, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 -0.0297135 -0.2187620 0.1593350 0.9997420
3-1 0.1760265 -0.0497545 0.4018074 0.2559558
4-1 -0.0935517 -0.3069977 0.1198943 0.8844942
5-1 0.0317821 -0.1888791 0.2524434 0.9998561
6-1 0.0220864 -0.2163995 0.2605722 0.9999929
8-1 0.0608814 -0.1687237 0.2904865 0.9926034
9-1 0.2095887 0.0088802 0.4102972 0.0335789
3-2 0.2057400 -0.0080663 0.4195462 0.0689808
4-2 -0.0638382 -0.2645750 0.1368986 0.9784025
5-2 0.0614956 -0.1468969 0.2698882 0.9859871
6-2 0.0517999 -0.1753819 0.2789816 0.9970964
8-2 0.0905949 -0.1272458 0.3084355 0.9101124
9-2 0.2393022 0.0521659 0.4264384 0.0028819
4-3 -0.2695781 -0.5052324 -0.0339238 0.0126588
5-3 -0.1442443 -0.3864532 0.0979646 0.6099023
6-3 -0.1539401 -0.4124923 0.1046121 0.6102031
8-3 -0.1151451 -0.3655293 0.1352391 0.8560854
9-3 0.0335622 -0.1906200 0.2577445 0.9998136
5-4 0.1253338 -0.1054199 0.3560876 0.7156915
6-4 0.1156380 -0.1322156 0.3634917 0.8465566
8-4 0.1544331 -0.0848876 0.3937538 0.5055836
9-4 0.3031404 0.0913862 0.5148945 0.0004385
6-5 -0.0096958 -0.2637895 0.2443979 1.0000000
8-5 0.0290993 -0.2166783 0.2748768 0.9999618
9-5 0.1778065 -0.0412186 0.3968317 0.2095874
8-6 0.0387950 -0.2231033 0.3006933 0.9998263
9-6 0.1875023 -0.0494706 0.4244752 0.2385336
9-8 0.1487073 -0.0793259 0.3767405 0.4913784

NULL

Week 7

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 3.34 7 0.48 5.10 < .001
Residuals 33.83 362 0.09
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(7, 362) = 5.10, p < .001; Eta2 = 0.09, 95% CI [0.04, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 -0.0230717 -0.1889887 0.1428454 0.9998864
3-1 0.2152655 0.0171105 0.4134205 0.0225346
4-1 -0.0748307 -0.2621600 0.1124986 0.9263311
5-1 0.0802111 -0.1134507 0.2738728 0.9118539
6-1 -0.0084373 -0.2177426 0.2008681 1.0000000
8-1 0.1808418 -0.0206694 0.3823530 0.1152735
9-1 0.1314010 -0.0479840 0.3107859 0.3343739
3-2 0.2383372 0.0506917 0.4259827 0.0031866
4-2 -0.0517590 -0.2279342 0.1244162 0.9863390
5-2 0.1032827 -0.0796115 0.2861769 0.6731047
6-2 0.0146344 -0.1847501 0.2140188 0.9999986
8-2 0.2039135 0.0127272 0.3950998 0.0272444
9-2 0.1544726 -0.0132306 0.3221758 0.0961295
4-3 -0.2900962 -0.4969165 -0.0832759 0.0006370
5-3 -0.1350545 -0.3476273 0.0775184 0.5263417
6-3 -0.2237028 -0.4506193 0.0032137 0.0565284
8-3 -0.0344237 -0.2541716 0.1853242 0.9997475
9-3 -0.0838646 -0.2835175 0.1157884 0.9056062
5-4 0.1550417 -0.0474776 0.3575611 0.2780653
6-4 0.0663934 -0.1511335 0.2839203 0.9829334
8-4 0.2556725 0.0456344 0.4657106 0.0057897
9-4 0.2062316 0.0173185 0.3951448 0.0213967
6-5 -0.0886483 -0.3116518 0.1343551 0.9281141
8-5 0.1006308 -0.1150741 0.3163356 0.8465597
9-5 0.0511899 -0.1440043 0.2463841 0.9930791
8-6 0.1892791 -0.0405740 0.4191323 0.1942406
9-6 0.1398382 -0.0708859 0.3505623 0.4676397
9-8 -0.0494409 -0.2524253 0.1535435 0.9956080

NULL

Week 8

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 3.46 7 0.49 6.41 < .001
Residuals 27.62 358 0.08
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(7, 358) = 6.41, p < .001; Eta2 = 0.11, 95% CI [0.05, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 0.0401325 -0.1106437 0.1909087 0.9924211
3-1 0.3042080 0.1194474 0.4889685 0.0000223
4-1 0.0287497 -0.1414847 0.1989842 0.9995837
5-1 0.0624397 -0.1135494 0.2384287 0.9603052
6-1 -0.0331105 -0.2253323 0.1591112 0.9995257
8-1 0.1128815 -0.0686798 0.2944427 0.5548361
9-1 0.1987388 0.0349275 0.3625501 0.0060570
3-2 0.2640754 0.0886100 0.4395409 0.0001657
4-2 -0.0113828 -0.1714810 0.1487154 0.9999989
5-2 0.0223071 -0.1438969 0.1885112 0.9999105
6-2 -0.0732431 -0.2565485 0.1100624 0.9262001
8-2 0.0727489 -0.0993445 0.2448424 0.9026281
9-2 0.1586063 0.0053554 0.3118571 0.0365801
4-3 -0.2754582 -0.4679016 -0.0830149 0.0004409
5-3 -0.2417683 -0.4393203 -0.0442163 0.0053875
6-3 -0.3373185 -0.5494597 -0.1251773 0.0000506
8-3 -0.1913265 -0.3938584 0.0112054 0.0796860
9-3 -0.1054692 -0.2922547 0.0813163 0.6731496
5-4 0.0336899 -0.1503484 0.2177283 0.9992924
6-4 -0.0618603 -0.2614779 0.1377573 0.9813581
8-4 0.0841317 -0.1052422 0.2735056 0.8769108
9-4 0.1699891 -0.0024410 0.3424191 0.0565244
6-5 -0.0955502 -0.3000974 0.1089970 0.8456217
8-5 0.0504418 -0.1441214 0.2450050 0.9935438
9-5 0.1362991 -0.0418146 0.3144129 0.2785490
8-6 0.1459920 -0.0633687 0.3553527 0.4001081
9-6 0.2318493 0.0376804 0.4260183 0.0074637
9-8 0.0858573 -0.0977642 0.2694788 0.8449695

NULL

Week 9

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 3.15 7 0.45 5.44 < .001
Residuals 28.29 342 0.08
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(7, 342) = 5.44, p < .001; Eta2 = 0.10, 95% CI [0.04, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 -0.0224002 -0.1785703 0.1337699 0.9998595
4-1 -0.0191770 -0.1955015 0.1571475 0.9999785
5-1 0.0210683 -0.1612166 0.2033532 0.9999674
6-1 -0.0368687 -0.2359671 0.1622296 0.9992349
7-1 0.2624926 0.0190701 0.5059150 0.0243649
8-1 0.1630569 -0.0249996 0.3511134 0.1439238
9-1 0.1819526 0.0131058 0.3507994 0.0245344
4-2 0.0032232 -0.1626024 0.1690488 1.0000000
5-2 0.0434685 -0.1286814 0.2156184 0.9944951
6-2 -0.0144685 -0.2043315 0.1753945 0.9999981
7-2 0.2848928 0.0489642 0.5208214 0.0064598
8-2 0.1854571 0.0072072 0.3637071 0.0347949
9-2 0.2043528 0.0465014 0.3622041 0.0024069
5-4 0.0402453 -0.1503769 0.2308675 0.9982124
6-4 -0.0176917 -0.2244505 0.1890671 0.9999959
7-4 0.2816696 0.0319427 0.5313965 0.0149399
8-4 0.1822339 -0.0139147 0.3783825 0.0902603
9-4 0.2011296 0.0233143 0.3789449 0.0144620
6-5 -0.0579370 -0.2698017 0.1539277 0.9910559
7-5 0.2414243 -0.0125462 0.4953947 0.0759152
8-5 0.1419886 -0.0595349 0.3435122 0.3858435
9-5 0.1608843 -0.0228430 0.3446116 0.1352844
7-6 0.2993613 0.0330656 0.5656569 0.0155157
8-6 0.1999256 -0.0169248 0.4167761 0.0954494
9-6 0.2188213 0.0184015 0.4192411 0.0213866
8-7 -0.0994356 -0.3575799 0.1587086 0.9386295
9-7 -0.0805400 -0.3250445 0.1639645 0.9735717
9-8 0.0188957 -0.1705593 0.2083506 0.9999881

NULL

Week 10

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 2.23 5 0.45 7.45 < .001
Residuals 16.38 274 0.06
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(5, 274) = 7.45, p < .001; Eta2 = 0.12, 95% CI [0.05, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 0.0125851 -0.1123287 0.1374989 0.9997263
4-1 0.0826406 -0.0583939 0.2236750 0.5450757
5-1 0.0491226 -0.0966793 0.1949245 0.9279838
6-1 0.0579639 -0.1012864 0.2172142 0.9023389
8-1 0.2864402 0.1360219 0.4368586 0.0000015
4-2 0.0700554 -0.0625814 0.2026922 0.6543591
5-2 0.0365375 -0.1011578 0.1742329 0.9736225
6-2 0.0453788 -0.1064845 0.1972421 0.9560655
8-2 0.2738551 0.1312806 0.4164296 0.0000012
5-4 -0.0335179 -0.1859885 0.1189526 0.9886329
6-4 -0.0246767 -0.1900542 0.1407009 0.9981557
8-4 0.2037997 0.0469088 0.3606906 0.0031764
6-5 0.0088413 -0.1606203 0.1783028 0.9999895
8-5 0.2373176 0.0761275 0.3985077 0.0004625
8-6 0.2284763 0.0550269 0.4019257 0.0026206

NULL

Week 11

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 1.01 4 0.25 4.24 0.002
Residuals 14.56 245 0.06
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(4, 245) = 4.24, p = 0.002; Eta2 = 0.06, 95% CI [0.01, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 0.0301881 -0.0890891 0.1494654 0.9573153
4-1 0.0690910 -0.0655795 0.2037614 0.6218616
5-1 0.0792649 -0.0599580 0.2184877 0.5215465
8-1 0.2022579 0.0586270 0.3458889 0.0013068
4-2 0.0389028 -0.0877490 0.1655546 0.9164937
5-2 0.0490767 -0.0824054 0.1805588 0.8432751
8-2 0.1720698 0.0359287 0.3082109 0.0054463
5-4 0.0101739 -0.1354167 0.1557644 0.9996947
8-4 0.1331669 -0.0166445 0.2829784 0.1075595
8-5 0.1229931 -0.0309235 0.2769097 0.1846365

NULL

Week 12

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 0.53 4 0.13 2.11 0.080
Residuals 15.34 245 0.06
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically not significant and small (F(4, 245) = 2.11, p = 0.080; Eta2 = 0.03, 95% CI [0.00, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 0.0381984 -0.0842157 0.1606125 0.9119637
4-1 0.0940212 -0.0441910 0.2322333 0.3366096
5-1 0.0971867 -0.0456976 0.2400709 0.3367443
8-1 0.1326446 -0.0147637 0.2800529 0.1002100
4-2 0.0558227 -0.0741599 0.1858053 0.7627018
5-2 0.0589883 -0.0759517 0.1939282 0.7506124
8-2 0.0944462 -0.0452753 0.2341676 0.3431487
5-4 0.0031655 -0.1462539 0.1525849 0.9999974
8-4 0.0386234 -0.1151279 0.1923748 0.9584369
8-5 0.0354579 -0.1225065 0.1934224 0.9723104

NULL

Week 13

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 1.81 5 0.36 5.70 < .001
Residuals 16.29 257 0.06
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(5, 257) = 5.70, p < .001; Eta2 = 0.10, 95% CI [0.04, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 0.0370334 -0.0916800 0.1657468 0.9624842
4-1 0.1076629 -0.0376615 0.2529872 0.2765917
5-1 0.0688905 -0.0813463 0.2191274 0.7756455
7-1 0.3413801 0.1184199 0.5643404 0.0002318
8-1 0.1843335 0.0293398 0.3393272 0.0095627
4-2 0.0706295 -0.0660418 0.2073008 0.6749114
5-2 0.0318571 -0.1100266 0.1737409 0.9874492
7-2 0.3043467 0.0869274 0.5217660 0.0010724
8-2 0.1473001 0.0003888 0.2942114 0.0489657
5-4 -0.0387723 -0.1958806 0.1183360 0.9807967
7-4 0.2337173 0.0060702 0.4613643 0.0403831
8-4 0.0766706 -0.0849925 0.2383337 0.7497897
7-5 0.2724896 0.0416755 0.5033036 0.0103921
8-5 0.1154429 -0.0506502 0.2815360 0.3475982
8-7 -0.1570467 -0.3909848 0.0768915 0.3877038

NULL

Week 14

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 0.99 4 0.25 5.06 < .001
Residuals 11.98 245 0.05
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(4, 245) = 5.06, p < .001; Eta2 = 0.08, 95% CI [0.02, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 0.0294628 -0.0787197 0.1376453 0.9447243
4-1 0.0950264 -0.0271174 0.2171703 0.2074139
5-1 0.0120160 -0.1142568 0.1382888 0.9989657
8-1 0.1871020 0.0568312 0.3173729 0.0009737
4-2 0.0655636 -0.0493074 0.1804347 0.5190274
5-2 -0.0174468 -0.1366989 0.1018053 0.9944687
8-2 0.1576392 0.0341615 0.2811169 0.0048288
5-4 -0.0830104 -0.2150586 0.0490378 0.4189383
8-4 0.0920756 -0.0438009 0.2279521 0.3405878
8-5 0.1750860 0.0354862 0.3146858 0.0059642

NULL

Week 15

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 1.21 4 0.30 5.81 < .001
Residuals 12.80 245 0.05
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(4, 245) = 5.81, p < .001; Eta2 = 0.09, 95% CI [0.03, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 0.0267661 -0.0850764 0.1386086 0.9650667
4-1 0.0932446 -0.0330316 0.2195208 0.2551072
5-1 -0.0038074 -0.1343522 0.1267374 0.9999906
8-1 0.2027312 0.0680530 0.3374094 0.0004621
4-2 0.0664785 -0.0522789 0.1852359 0.5385348
5-2 -0.0305735 -0.1538601 0.0927131 0.9603078
8-2 0.1759651 0.0483099 0.3036202 0.0017743
5-4 -0.0970520 -0.2335676 0.0394636 0.2920134
8-4 0.1094866 -0.0309869 0.2499600 0.2058193
8-5 0.2065386 0.0622159 0.3508613 0.0010283

NULL

Week 16

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 0.89 4 0.22 4.41 0.002
Residuals 12.40 245 0.05
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(4, 245) = 4.41, p = 0.002; Eta2 = 0.07, 95% CI [0.02, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 0.0321875 -0.0778866 0.1422616 0.9293241
4-1 0.1009875 -0.0232921 0.2252671 0.1710322
5-1 0.0474709 -0.0810098 0.1759516 0.8481297
8-1 0.1843869 0.0518382 0.3169357 0.0015590
4-2 0.0688000 -0.0480797 0.1856796 0.4875017
5-2 0.0152834 -0.1060538 0.1366206 0.9969008
8-2 0.1521994 0.0265627 0.2778362 0.0088273
5-4 -0.0535165 -0.1878737 0.0808406 0.8092031
8-4 0.0833995 -0.0548528 0.2216518 0.4621021
8-5 0.1369160 -0.0051247 0.2789568 0.0648619

NULL

Week 17

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 1.61 4 0.40 7.53 < .001
Residuals 13.05 244 0.05
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(4, 244) = 7.53, p < .001; Eta2 = 0.11, 95% CI [0.05, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 0.0704788 -0.0426551 0.1836126 0.4284220
4-1 0.1350524 0.0073182 0.2627867 0.0323567
5-1 0.0018939 -0.1301582 0.1339461 0.9999994
8-1 0.2378244 0.1004200 0.3752289 0.0000330
4-2 0.0645737 -0.0555549 0.1847023 0.5782204
5-2 -0.0685848 -0.1932949 0.0561253 0.5560432
8-2 0.1673457 0.0369815 0.2977099 0.0045184
5-4 -0.1331585 -0.2712504 0.0049334 0.0646988
8-4 0.1027720 -0.0404467 0.2459907 0.2827725
8-5 0.2359305 0.0888478 0.3830132 0.0001512

NULL

Week 18

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 1.05 4 0.26 5.05 < .001
Residuals 12.77 245 0.05
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(4, 245) = 5.05, p < .001; Eta2 = 0.08, 95% CI [0.02, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 0.0743556 -0.0373484 0.1860596 0.3590665
4-1 0.1185116 -0.0076082 0.2446315 0.0767841
5-1 0.0256495 -0.1047337 0.1560326 0.9829853
8-1 0.1998101 0.0652986 0.3343215 0.0005743
4-2 0.0441561 -0.0744542 0.1627664 0.8445406
5-2 -0.0487061 -0.1718400 0.0744278 0.8131128
8-2 0.1254545 -0.0020426 0.2529516 0.0562001
5-4 -0.0928622 -0.2292088 0.0434844 0.3353906
8-4 0.0812984 -0.0590011 0.2215979 0.5036312
8-5 0.1741606 0.0300166 0.3183046 0.0090839

NULL

Week 19

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 3.67 3 1.22 13.96 < .001
Residuals 17.54 200 0.09
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(3, 200) = 13.96, p < .001; Eta2 = 0.17, 95% CI [0.09, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 0.0463792 -0.0902151 0.1829734 0.8154064
5-1 0.0592179 -0.1002176 0.2186534 0.7710052
8-1 0.3853322 0.2194344 0.5512299 0.0000000
5-2 0.0128387 -0.1377322 0.1634097 0.9961849
8-2 0.3389530 0.1815554 0.4963506 0.0000005
8-5 0.3261143 0.1485313 0.5036972 0.0000221

NULL

Week 20

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 1.28 3 0.43 8.02 < .001
Residuals 10.62 199 0.05
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(3, 199) = 8.02, p < .001; Eta2 = 0.11, 95% CI [0.04, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 -0.0107738 -0.1173317 0.0957842 0.9936859
5-1 -0.0318647 -0.1562413 0.0925119 0.9105598
8-1 0.1987025 0.0681268 0.3292782 0.0006412
5-2 -0.0210909 -0.1385522 0.0963703 0.9665471
8-2 0.2094762 0.0854697 0.3334828 0.0001139
8-5 0.2305672 0.0909514 0.3701830 0.0001703

NULL

Week 21

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 0.49 3 0.16 1.19 0.315
Residuals 27.82 201 0.14
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically not significant and small (F(3, 201) = 1.19, p = 0.315; Eta2 = 0.02, 95% CI [0.00, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 -0.0088091 -0.1803826 0.1627645 0.9991573
5-1 -0.0110215 -0.2112856 0.1892426 0.9989632
8-1 0.1221026 -0.0845024 0.3287076 0.4208793
5-2 -0.0022124 -0.1913419 0.1869171 0.9999900
8-2 0.1309117 -0.0649196 0.3267429 0.3099365
8-5 0.1331241 -0.0882762 0.3545244 0.4052391

NULL

Week 22

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 1.83 2 0.91 15.40 < .001
Residuals 9.55 161 0.06
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(2, 161) = 15.40, p < .001; Eta2 = 0.16, 95% CI [0.08, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 0.1018006 -0.0007470 0.2043482 0.0521598
8-1 0.2917718 0.1672246 0.4163189 0.0000004
8-2 0.1899711 0.0718055 0.3081367 0.0005908

NULL

Week 23

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 1.51 2 0.76 11.49 < .001
Residuals 10.46 159 0.07
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(2, 159) = 11.49, p < .001; Eta2 = 0.13, 95% CI [0.05, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 0.0603122 -0.0477256 0.1683500 0.3857652
8-1 0.2652194 0.1315964 0.3988423 0.0000169
8-2 0.2049072 0.0778799 0.3319345 0.0005650

NULL

Week 24

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 1.13 2 0.57 9.38 < .001
Residuals 9.59 159 0.06
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(2, 159) = 9.38, p < .001; Eta2 = 0.11, 95% CI [0.04, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 0.0118620 -0.0915551 0.1152792 0.9602294
8-1 0.2138422 0.0859342 0.3417503 0.0003366
8-2 0.2019802 0.0803857 0.3235747 0.0003704

NULL

Week 25

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 1.24 2 0.62 12.35 < .001
Residuals 7.99 159 0.05
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(2, 159) = 12.35, p < .001; Eta2 = 0.13, 95% CI [0.06, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 0.0774245 -0.0170154 0.1718643 0.1310160
8-1 0.2445609 0.1277561 0.3613657 0.0000055
8-2 0.1671364 0.0560971 0.2781758 0.0014087

NULL

Week 26

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 1.25 2 0.63 9.81 < .001
Residuals 10.15 159 0.06
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(2, 159) = 9.81, p < .001; Eta2 = 0.11, 95% CI [0.04, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 0.0334467 -0.0729898 0.1398831 0.7380133
8-1 0.2344008 0.1027585 0.3660432 0.0001244
8-2 0.2009542 0.0758097 0.3260986 0.0006021

NULL

Week 27

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 1.23 2 0.62 13.72 < .001
Residuals 7.14 159 0.04
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(2, 159) = 13.72, p < .001; Eta2 = 0.15, 95% CI [0.07, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 0.0791902 -0.0100476 0.1684280 0.0931648
8-1 0.2438177 0.1334469 0.3541885 0.0000016
8-2 0.1646275 0.0597046 0.2695504 0.0008254

NULL

Week 28

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 1.35 2 0.67 12.39 < .001
Residuals 8.66 159 0.05
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(2, 159) = 12.39, p < .001; Eta2 = 0.13, 95% CI [0.06, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 0.0938740 -0.0044165 0.1921644 0.0646129
8-1 0.2557312 0.1341639 0.3772984 0.0000050
8-2 0.1618572 0.0462905 0.2774239 0.0032555

NULL

Week 29

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 1.21 2 0.60 13.26 < .001
Residuals 7.25 159 0.05
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(2, 159) = 13.26, p < .001; Eta2 = 0.14, 95% CI [0.06, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 0.1168061 0.0268933 0.2067188 0.0069946
8-1 0.2390735 0.1278679 0.3502791 0.0000030
8-2 0.1222675 0.0165510 0.2279840 0.0188720

NULL

Week 30

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 1.24 2 0.62 12.05 < .001
Residuals 8.18 159 0.05
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(2, 159) = 12.05, p < .001; Eta2 = 0.13, 95% CI [0.06, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 0.0910687 -0.0044933 0.1866308 0.0653701
8-1 0.2451968 0.1270040 0.3633896 0.0000067
8-2 0.1541281 0.0417693 0.2664868 0.0040645

NULL

Week 31

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 1.18 2 0.59 10.23 < .001
Residuals 9.18 159 0.06
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(2, 159) = 10.23, p < .001; Eta2 = 0.11, 95% CI [0.04, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 0.0972197 -0.0039996 0.1984391 0.0626807
8-1 0.2390992 0.1139095 0.3642890 0.0000359
8-2 0.1418795 0.0228691 0.2608899 0.0148649

NULL

Week 32

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 0.91 2 0.45 6.20 0.003
Residuals 11.62 159 0.07
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(2, 159) = 6.20, p = 0.003; Eta2 = 0.07, 95% CI [0.02, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 0.0991883 -0.0146736 0.2130501 0.1013685
8-1 0.2074630 0.0666367 0.3482892 0.0018296
8-2 0.1082747 -0.0256003 0.2421498 0.1382025

NULL

Week 33

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 0.70 2 0.35 6.55 0.002
Residuals 8.44 158 0.05
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(2, 158) = 6.55, p = 0.002; Eta2 = 0.08, 95% CI [0.02, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 0.1109694 0.0133113 0.2086274 0.0215593
8-1 0.1726119 0.0521780 0.2930458 0.0025206
8-2 0.0616425 -0.0530883 0.1763734 0.4135427

NULL

Week 34

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 1.97 2 0.98 14.61 < .001
Residuals 10.65 158 0.07
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(2, 158) = 14.61, p < .001; Eta2 = 0.16, 95% CI [0.07, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 0.1868757 0.0772095 0.2965418 0.0002523
8-1 0.2890028 0.1537603 0.4242454 0.0000035
8-2 0.1021271 -0.0267111 0.2309654 0.1491873

NULL

Week 35

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 1.89 2 0.95 12.28 < .001
Residuals 12.10 157 0.08
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and medium (F(2, 157) = 12.28, p < .001; Eta2 = 0.14, 95% CI [0.06, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 0.1592373 0.0419567 0.2765179 0.0045259
8-1 0.2978010 0.1517626 0.4438395 0.0000098
8-2 0.1385637 -0.0006949 0.2778224 0.0514706

NULL

Week 36

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 2.29 2 1.14 16.11 < .001
Residuals 11.07 156 0.07
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(2, 156) = 16.11, p < .001; Eta2 = 0.17, 95% CI [0.09, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
2-1 0.1740027 0.0605118 0.2874936 0.0011201
8-1 0.3268445 0.1875465 0.4661424 0.0000004
8-2 0.1528418 0.0203129 0.2853706 0.0192950

NULL

Week 37

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 2.22 1 2.22 26.73 < .001
Residuals 6.98 84 0.08
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(1, 84) = 26.73, p < .001; Eta2 = 0.24, 95% CI [0.12, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
8-1 0.3303645 0.2032909 0.4574382 1.6e-06

NULL

Week 38

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 2.64 1 2.64 26.55 < .001
Residuals 8.25 83 0.10
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(1, 83) = 26.55, p < .001; Eta2 = 0.24, 95% CI [0.12, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
8-1 0.3614293 0.2219095 0.5009491 1.7e-06

NULL

Week 39

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 2.76 1 2.76 21.98 < .001
Residuals 10.17 81 0.13
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(1, 81) = 21.98, p < .001; Eta2 = 0.21, 95% CI [0.10, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
8-1 0.3746446 0.2156293 0.53366 1.1e-05

NULL

Week 40

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 2.53 1 2.53 21.96 < .001
Residuals 9.09 79 0.12
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(1, 79) = 21.96, p < .001; Eta2 = 0.22, 95% CI [0.10, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
8-1 0.3633933 0.2090524 0.5177342 1.14e-05

NULL

Week 41

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 1.90 1 1.90 16.80 < .001
Residuals 7.13 63 0.11
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(1, 63) = 16.80, p < .001; Eta2 = 0.21, 95% CI [0.08, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
8-1 0.3420326 0.1752971 0.5087681 0.0001208

NULL

Week 42

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 1.63 1 1.63 14.11 < .001
Residuals 6.94 60 0.12
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(1, 60) = 14.11, p < .001; Eta2 = 0.19, 95% CI [0.06, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
8-1 0.324597 0.1517682 0.4974258 0.0003915

NULL

Week 43

Model Summary
Parameter Sum_Squares df Mean_Square F p
datasetcode 1.66 1 1.66 16.60 < .001
Residuals 5.58 56 0.10
Anova Table (Type 1 tests)
Testing model assumption of residual normality with QQ plot

The ANOVA (formula: i ~ datasetcode) suggests that:

  • The main effect of datasetcode is statistically significant and large (F(1, 56) = 16.60, p < .001; Eta2 = 0.23, 95% CI [0.09, 1.00])
Effect sizes were labelled following Field’s (2013) recommendations.
Statistical significance (P < 0.5) means we reject the null hypothesis that the means are equal.
Contrast Difference Lower CI Upper CI P-value
8-1 0.3386913 0.1721746 0.505208 0.0001468

NULL